Innovative Decision Support System based Artificial Innovative Decision Support System based Artificial Intelligence amp Spatial Planning for Disaster Risk ReductionIntelligence amp Spatial Planning for Disaster Risk Reduction

Session VISession VI Reduction of Risk in Environmental Disaster Reduction of Risk in Environmental Disaster

Dr Hussain Aziz SALEH12 amp Prof Georges ALLAERT2

1Ministry of Local Administration and Environment Damascus SyriaTel+963 11 211 9955 Fax+963 11 2119954 E-mailHussainSalehUGentbe

2Institute for Sustainable Mobility Faculty of EngineeringGhent University

Krijgslaan 281 (IDM S8) B-9000 Gent Belgium Tel+32 9 264 47 17 Fax+32 9 264 49 86 E-mailGeorgesAllaertUGentbe

Contents of the PaperContents of the Paper

bull Introduction to Processing Architecture of the Disaster Management Information System

bull Information Flow amp Early WarningSystem Architecture in Flood Management

bull Geomatic amp Information Communication Technologybull Multiobjective Combinatorial Optimisation Problems

(MCOPs)bull Metaheuristic Techniquesbull Disaster Warning Network for the Danube Basinbull Optimiasting some real-life applications (Earthquake amp

Flooding)bull Conclusion and future work

Disaster Management CycleDisaster Management Cycle

Post Disaster-Lessons learnet-Scenario update-Socio-economic amp environment impact assessment-Spatial (re)planning

Recovery (Rehabilitation amp Reconstruction)-Early damage assessment-Re-establishing life-lines transport amp communication infrastructure-Reinforcement

Alert-Real time monitoring amp forcasting-Early warning-Secure amp dependable telecom-Scenario identification-all media alarm

Prevention amp Mitigation-Hazard prediction amp modelling-Risk assessment amp mapping-Spatial planning-Structural amp non-structural measures-Public Awareness amp Education

Response-Dispatching of resources -Emergency telecom-Situational awareness-Command control coordination-Information dissemination-Emergency healthcare

Preparendness-Scenarios develpmentEmeregency PlanningTraining

Disaster

Distribution to the UsersDistribution to the Users

Data collection

SpaceAirborne ground

Ancillary

Dynamic Data

Processor

Main Services (File Spatial Data Engine Internet Map Web etc)

Desktop GIS Tools

InternetIPhttp

Information Flow amp Early WarningInformation Flow amp Early WarningSystem Architecture in Flood ManagementSystem Architecture in Flood Management

Risk Assessmen

t

Early Warning

Monitoring

Damage Assessmen

t

Relief provision

Web site bulletin e-mail fax radio telephone etc

Dissemination

Hydrological Stations Provision of Forecasts

Flood Management amp Risk Reduction (FMRR)Flood Management amp Risk Reduction (FMRR)

1 Regional FMRR 1 Regional FMRR centrecentre

5 Land Use management5 Land Use management

4 Flood Emergency Mngmt4 Flood Emergency Mngmt

3 Trans-boundary mediation3 Trans-boundary mediation

2 Structural Measures amp2 Structural Measures ampFlood ProofingFlood Proofing

Capacity BuildingCapacity Building(Training Unit)(Training Unit)

Data Collection Data Collection amp Processingamp Processing

Forecasting Forecasting Warning amp Warning amp DisseminationDissemination

Annual Flood Forum Annual Flood Forum Workshops Workshops CommunicationsCommunications

FMRRFMRR

6 Spatial Planning6 Spatial Planning

Disaster reduction as part of sustainable development through1048707 Strengthen institutions (especially in communities) to build resilience1048707 Build risk reduction into emergency management and recovery

Remote Sensing Technology

IRS-P6 AWiFS Image of 21-Dec-04 IRS-P6 AWiFS Image of 26-Dec-04

A Close View of Camorta Island Tsunami - 2004

Pre-Event Post-Event

Global Navigation Satellite Systems GNSSsGlobal Navigation Satellite Systems GNSSs

GALILILEO

Sat2

Receiver

X

Z

Y(0 0 0)

WGS-84

Local

Sat3

Sat1

Sat4

GPS GLONASS

Geographic Information ProcessGeographic Information Process Database amp VisualizationDatabase amp Visualization

The geographic information process consists of three stages Data acquisition data processing and data dissemination

Database ldquoNot easy to interpretrdquo

Visualisation ldquoWorth a Thousand wordsrdquo

Rules of Spatial Planning Rules of Spatial Planning

in Flood Managementin Flood Management flood reduction is part of sustainable development and the various action possibilities

to improve preventive flood protection based on spatial planning and urban development can be described as follow

bull Protection of existing retention areas eg declaration of flood areas etcbull Extension of retention areas eg creating detention ponds etcbull Retention in the catchments eg restoration of small streams etcbull Minimisation of damage potential eg preventive land-use management etcbull Technical flood protection measures eg dikes retention ponds etc

As shown from above flood risk can only effectively be reduced if in addition to the technical measures spatial planning regulates land-use in flood-prone areas

Flood Forecasting amp Early Warning

Provide accurate Provide accurate forecasts with a forecasts with a suitable lead time suitable lead time and a timely and and a timely and effective effective disseminationdissemination

Heavy rainsnowmelt or high inflow

Data collection and transmission

Boundary estimation (rainfall tide )

Forecast calculation

Warning dissemination

Emergency action

Flooding starts

time

Meteorological forecast

Min

imis

em

axim

ise

Multiobjective Combinatorial Optimisation Multiobjective Combinatorial Optimisation Problems (MCOPs)Problems (MCOPs)

Many real-life applications involves two types of problem difficulty 1) multiple conflicting objectives 2) a highly complex search domain

Xnx2x1xxtosubjectxnfx2fx1fxfoptimizeOPCM

X is the search domain fi(x) is the ith objective function to be optimised

x is a set of decision vectors

Minimize cost

Question How do I combine all the fieldwork components and find the optimal schedule for receivers Therefore we need to answer not only the question

ldquo What is the best rdquo but also ldquoWhat is sufficiently robustrdquoAnswer Metaheuristic Techniques

MaximisationMaximisation(Global and local maxima)(Global and local maxima) Objective function

Iteration

Global maximum

Local maximum

In mechanical engineering an engineer wishes to design a car consisting of composite materials The engineer by optimization (maximization in this case) will conceivably design a lighter stronger attractive and safer composite car

In GPS surveying a surveyor wants to design a network taking in consideration components of the field-work The GPS surveyor by optimisation (minimization in this case) will design a cheaper and more acceptable schedule

Objective function

Iteration

Global minimum

Local minimum

MinimizationMinimization(Global and local minima)(Global and local minima)

Schematic Representation of the Search Schematic Representation of the Search Progress of Metaheuristic TechniquesProgress of Metaheuristic Techniques

S0

S2

S3

S4

S5

S6

S7

S8

S9

S10

S11

S12I(S0)

I(S4) I(S9)

I(S6)I(S12)

Q(S)

S1

Iterations

Values of Si

or the objective function

Select a given solution S I(S) and compute its value C(S) Generate a schedule Srsquo I(S) and compute its value C(Srsquo)If C(Srsquo) lt C(S) then Srsquo replaces S as a current solutionOtherwise retain S and generate other moves until C(Srsquo)ltC(S) for all Srsquo I(S)Terminate the search and return S as the local optimal solution

Q(S)I(S0)

S0

Metaheuristic TechniquesMetaheuristic Techniques bull Metaheuristic technique is an iterative self-learning procedure for

quickly and efficiently identifying a high quality solution for COPs

bull Types of Metaheuristics 1- Solution improvement techniques Simulated Annealing SA Tabu Search

TS 2- Solution construction techniques Ant Colony Optimisation ACO Genetic

Algorithm GA Neural Network NNbull Fundamental concepts of the

metaheuristics 1- Selection or construction of the initial

solution 2- Generation of the neighbouring solutions 3- Acceptance of solutions 4- Stopping criteria

Initial Solution Formation

Neighbourhood

Search by Move Formation(Local Search)

Provisional Neighbourhood Formation

Search by Solution Formation(Development and guided search)

Neighbourhood for the nextsolution formation

Acceptance Criteria

Termination of the Search

The Simulated Annealing (SA) TechniqueThe Simulated Annealing (SA) Technique

Crystalline solid (frozen) state (T=0) Amorphous solid (frozen) state (T=0)

Ucirc High free energy (high temperature)

The material as a system of particles(Cooling process)

Annealing (slow cooling) Quenching (fast cooling)

Ucirc Ucirc Global minimum of the energy Local minimum of the energy

Case II Case III

Case I

bull Based on analogy between combinatorial optimization and annealing process of solids

bull Improvement of solution for move from S to Srsquo always accepted

bull Accepts uphill move only with given probability (decreases in a number of rounds to zero)

bull Cooling parameters the initial starting value of the temperature the temperature length (Markov Chain) L the cooling ratio F

Temperature

Iterations

Stop

StartK

F

LTi

Tf

The Tabu Search (TS) techniqueThe Tabu Search (TS) technique

bull Add memory to LS (Prevent Cycling increase diversity of exploration encourage escape from local optima)

bull Avoids cycling (visiting same solution more than once) by use of short term memory for the very recent history and long-term memory for the distant history

bull TS parameters Size of Memry Contents of memory (solution based attributes eg changes to solution features) long term memory

bull Tabu Listbull Candidate Listbull Tabu Tenure

The modified ACO will have some major differences with a real (natural) one

bull Artificial ants will have some memorybull Artificial ants will not be completely blindbull Artificial ants will live in an environment

where time is discrete

The Ant Colony Optimization The Ant Colony Optimization ACOACO

ACOACO

We will finish on time Do not worry OK

bull Construction the Initial Schedule

bull Local Searchbull Local Updating Rule

bull Global Updating Rule

otherwise0

(r)Jsifrηrτsrηsrτ

s)(rPk

(r)Ju

β

β

kk

uu

srΔτρsrτρ1srτ

otherwise0

srifLs)(rΔτ

srΔταsrτα1srτ1

gb tourbestglobalwhere

The Genetic Algorithms (GAs)The Genetic Algorithms (GAs) The evolution process for generating a populationThe evolution process for generating a population

Father

Mother

Mutation

SelectionMating

Child 1

Next Generation

Crossover

Initial Generation

N

Chromosome

Gene

One-flip mutation in child 2

Child 2

One- point crossover

Neural Networks NNNeural Networks NN imitate the human brain imitate the human brain

NN consists of a collection of interconnected simple computational units that work together cooperatively (like synapses in the human brain) These units are input units for receiving information from the problem domain hidden units as internal weight structure and output units for broadcasting data

Different types of the GPS Surveying Different types of the GPS Surveying NetworksNetworks

The Seychelles GPS Network (linear) The Malta GPS Network (triangulation)

Handicapped Person Transportation in the City of Handicapped Person Transportation in the City of Bruxelles using Grouping Genetic AlgorithmBruxelles using Grouping Genetic Algorithm

D1

D2

(n)

O2

O1

(m)

D

O

Depot1

Depot2

The covered area by Depot2The covered area by Depot 1

0

2

3 1

4

LocationDirection of vehicleContour of networkRegion of massif influx

[Ostarti =1000 Oendi =1015] [Dstarti =1100 Dendi =1130]

Desired departure interval=15 mins Desired arrival interval=30 mins

Travel time=0060 mins [1010 -1110]

1110 1010

Oi (home) Di (hospital)

Disaster Warning Network for Danube Disaster Warning Network for Danube Basin (DWN)Basin (DWN)

bull DWN is a system of satellites and ground stations for providing real time early warning of the impact of a disaster

bull DWN implements a network of Control Stations (CSs) spread over the whole geographic area of the hazard and transmits their observations to a master station

bull DWN provides this reliable information on a continuous basis through the parallel process of coverage accuracy prediction and integrity risk simulation functions

Disaster Warning Network Disaster Warning Network for the Danube Basinfor the Danube Basin

Reference Receiver RR

Roving Receiver 2

Roving Receiver 3

Roving Receiver 1

SatelliteGPS Signal 1

Error correction message 1

Roving Receiver 4

GPS Signal 4

GPS Signal RR

GPS Signal 2

GPS Signal 3

Error correction message 4

Error correction message 2

Error correction message 3

Differential GPSDifferential GPS

The DWN model for Environmental The DWN model for Environmental Pollution ControlPollution Control

bull Multiobjective Optimisation Problems (MOPs)

bull f(x) is the objective function to be optimised and X is the search domain

bull Ps is a multiplier for measuring the size of the user population near the location s (eg safety teams)bull f(acy ing) are the evaluations of accuracy coverage and integrity performance respectively for user location

sbull f(cost) is the cost function of a given DWN designbull N is the number of user locationsbull n is the number of obtained DWN designs

Xxxxxtosubject

xfxfxfxfoptimizeMOP

n

n

21

21

CostfffPnF n

N

s

sing

sacysDWN

1

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

bull Dynamic Metahuristics Algorithms (DMA) potentially have these capabilities to produce set of high quality real-time designs that can model more closely and easily the objective functions and tolerate the noisy evaluations given by these functions

bull The final design should be robust (ie performs well over a wide range of environment conditions) and flexible (ie allows easy adaptation after the environment has changed)

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

bull DMAs can successfully handle a mix of continuous and discrete parameters as well as selecting individual components from database Therefore another objective of this project is to connect DWN to a database which includes all the related information (physical geographicaland biochemical) colleted by other observation techniques (eg Geographic Information System (GIS) Remote Sensing (RS) photogrametery internet air (water and soil) survey technology etc)

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

DWN can effectively optimise this problem by 1) providing access to a wide range of data types in real-time 2) combining the observational data with innovative data analysis to improve forecasting and risk assessment 3) and being a user friendly tool in decision support of environmental management

Current Applications (Earthquake) (1)Current Applications (Earthquake) (1)

bull GEO-INFORMATION TECHNOLOGY AND ARTIFICIAL INTELLIGENCE FOR OPTIMIZING THE DISPLACEMENTS OF EARTHQUAKES A CASE STUDY FOR SYRIAN ACTIVE FAULT ZONES

bull With Dr Mohamad Rukieh Syrian Ministry of Commuincation and Technology

bull International Union of Geodesy and Geophysics IUGG07bull Perugia Italy July 3-14 2007bull httpwwwiugg2007perugiait

Disaster Management CycleDisaster Management Cycle

Post Disaster-Lessons learnet-Scenario update-Socio-economic amp environment impact assessment-Spatial (re)planning

Recovery (Rehabilitation amp Reconstruction)-Early damage assessment-Re-establishing life-lines transport amp communication infrastructure-Reinforcement

Alert-Real time monitoring amp forcasting-Early warning-Secure amp dependable telecom-Scenario identification-all media alarm

Prevention amp Mitigation-Hazard prediction amp modelling-Risk assessment amp mapping-Spatial planning-Structural amp non-structural measures-Public Awareness amp Education

Response-Dispatching of resources -Emergency telecom-Situational awareness-Command control coordination-Information dissemination-Emergency healthcare

Preparendness-Scenarios develpmentEmeregency PlanningTraining

Disaster

Distribution to the UsersDistribution to the Users

Data collection

SpaceAirborne ground

Ancillary

Dynamic Data

Processor

Main Services (File Spatial Data Engine Internet Map Web etc)

Desktop GIS Tools

InternetIPhttp

Information Flow amp Early WarningInformation Flow amp Early WarningSystem Architecture in Flood ManagementSystem Architecture in Flood Management

Risk Assessmen

t

Early Warning

Monitoring

Damage Assessmen

t

Relief provision

Web site bulletin e-mail fax radio telephone etc

Dissemination

Hydrological Stations Provision of Forecasts

Flood Management amp Risk Reduction (FMRR)Flood Management amp Risk Reduction (FMRR)

1 Regional FMRR 1 Regional FMRR centrecentre

5 Land Use management5 Land Use management

4 Flood Emergency Mngmt4 Flood Emergency Mngmt

3 Trans-boundary mediation3 Trans-boundary mediation

2 Structural Measures amp2 Structural Measures ampFlood ProofingFlood Proofing

Capacity BuildingCapacity Building(Training Unit)(Training Unit)

Data Collection Data Collection amp Processingamp Processing

Forecasting Forecasting Warning amp Warning amp DisseminationDissemination

Annual Flood Forum Annual Flood Forum Workshops Workshops CommunicationsCommunications

FMRRFMRR

6 Spatial Planning6 Spatial Planning

Disaster reduction as part of sustainable development through1048707 Strengthen institutions (especially in communities) to build resilience1048707 Build risk reduction into emergency management and recovery

Remote Sensing Technology

IRS-P6 AWiFS Image of 21-Dec-04 IRS-P6 AWiFS Image of 26-Dec-04

A Close View of Camorta Island Tsunami - 2004

Pre-Event Post-Event

Global Navigation Satellite Systems GNSSsGlobal Navigation Satellite Systems GNSSs

GALILILEO

Sat2

Receiver

X

Z

Y(0 0 0)

WGS-84

Local

Sat3

Sat1

Sat4

GPS GLONASS

Geographic Information ProcessGeographic Information Process Database amp VisualizationDatabase amp Visualization

The geographic information process consists of three stages Data acquisition data processing and data dissemination

Database ldquoNot easy to interpretrdquo

Visualisation ldquoWorth a Thousand wordsrdquo

Rules of Spatial Planning Rules of Spatial Planning

in Flood Managementin Flood Management flood reduction is part of sustainable development and the various action possibilities

to improve preventive flood protection based on spatial planning and urban development can be described as follow

bull Protection of existing retention areas eg declaration of flood areas etcbull Extension of retention areas eg creating detention ponds etcbull Retention in the catchments eg restoration of small streams etcbull Minimisation of damage potential eg preventive land-use management etcbull Technical flood protection measures eg dikes retention ponds etc

As shown from above flood risk can only effectively be reduced if in addition to the technical measures spatial planning regulates land-use in flood-prone areas

Flood Forecasting amp Early Warning

Provide accurate Provide accurate forecasts with a forecasts with a suitable lead time suitable lead time and a timely and and a timely and effective effective disseminationdissemination

Heavy rainsnowmelt or high inflow

Data collection and transmission

Boundary estimation (rainfall tide )

Forecast calculation

Warning dissemination

Emergency action

Flooding starts

time

Meteorological forecast

Min

imis

em

axim

ise

Multiobjective Combinatorial Optimisation Multiobjective Combinatorial Optimisation Problems (MCOPs)Problems (MCOPs)

Many real-life applications involves two types of problem difficulty 1) multiple conflicting objectives 2) a highly complex search domain

Xnx2x1xxtosubjectxnfx2fx1fxfoptimizeOPCM

X is the search domain fi(x) is the ith objective function to be optimised

x is a set of decision vectors

Minimize cost

Question How do I combine all the fieldwork components and find the optimal schedule for receivers Therefore we need to answer not only the question

ldquo What is the best rdquo but also ldquoWhat is sufficiently robustrdquoAnswer Metaheuristic Techniques

MaximisationMaximisation(Global and local maxima)(Global and local maxima) Objective function

Iteration

Global maximum

Local maximum

In mechanical engineering an engineer wishes to design a car consisting of composite materials The engineer by optimization (maximization in this case) will conceivably design a lighter stronger attractive and safer composite car

In GPS surveying a surveyor wants to design a network taking in consideration components of the field-work The GPS surveyor by optimisation (minimization in this case) will design a cheaper and more acceptable schedule

Objective function

Iteration

Global minimum

Local minimum

MinimizationMinimization(Global and local minima)(Global and local minima)

Schematic Representation of the Search Schematic Representation of the Search Progress of Metaheuristic TechniquesProgress of Metaheuristic Techniques

S0

S2

S3

S4

S5

S6

S7

S8

S9

S10

S11

S12I(S0)

I(S4) I(S9)

I(S6)I(S12)

Q(S)

S1

Iterations

Values of Si

or the objective function

Select a given solution S I(S) and compute its value C(S) Generate a schedule Srsquo I(S) and compute its value C(Srsquo)If C(Srsquo) lt C(S) then Srsquo replaces S as a current solutionOtherwise retain S and generate other moves until C(Srsquo)ltC(S) for all Srsquo I(S)Terminate the search and return S as the local optimal solution

Q(S)I(S0)

S0

Metaheuristic TechniquesMetaheuristic Techniques bull Metaheuristic technique is an iterative self-learning procedure for

quickly and efficiently identifying a high quality solution for COPs

bull Types of Metaheuristics 1- Solution improvement techniques Simulated Annealing SA Tabu Search

TS 2- Solution construction techniques Ant Colony Optimisation ACO Genetic

Algorithm GA Neural Network NNbull Fundamental concepts of the

metaheuristics 1- Selection or construction of the initial

solution 2- Generation of the neighbouring solutions 3- Acceptance of solutions 4- Stopping criteria

Initial Solution Formation

Neighbourhood

Search by Move Formation(Local Search)

Provisional Neighbourhood Formation

Search by Solution Formation(Development and guided search)

Neighbourhood for the nextsolution formation

Acceptance Criteria

Termination of the Search

The Simulated Annealing (SA) TechniqueThe Simulated Annealing (SA) Technique

Crystalline solid (frozen) state (T=0) Amorphous solid (frozen) state (T=0)

Ucirc High free energy (high temperature)

The material as a system of particles(Cooling process)

Annealing (slow cooling) Quenching (fast cooling)

Ucirc Ucirc Global minimum of the energy Local minimum of the energy

Case II Case III

Case I

bull Based on analogy between combinatorial optimization and annealing process of solids

bull Improvement of solution for move from S to Srsquo always accepted

bull Accepts uphill move only with given probability (decreases in a number of rounds to zero)

bull Cooling parameters the initial starting value of the temperature the temperature length (Markov Chain) L the cooling ratio F

Temperature

Iterations

Stop

StartK

F

LTi

Tf

The Tabu Search (TS) techniqueThe Tabu Search (TS) technique

bull Add memory to LS (Prevent Cycling increase diversity of exploration encourage escape from local optima)

bull Avoids cycling (visiting same solution more than once) by use of short term memory for the very recent history and long-term memory for the distant history

bull TS parameters Size of Memry Contents of memory (solution based attributes eg changes to solution features) long term memory

bull Tabu Listbull Candidate Listbull Tabu Tenure

The modified ACO will have some major differences with a real (natural) one

bull Artificial ants will have some memorybull Artificial ants will not be completely blindbull Artificial ants will live in an environment

where time is discrete

The Ant Colony Optimization The Ant Colony Optimization ACOACO

ACOACO

We will finish on time Do not worry OK

bull Construction the Initial Schedule

bull Local Searchbull Local Updating Rule

bull Global Updating Rule

otherwise0

(r)Jsifrηrτsrηsrτ

s)(rPk

(r)Ju

β

β

kk

uu

srΔτρsrτρ1srτ

otherwise0

srifLs)(rΔτ

srΔταsrτα1srτ1

gb tourbestglobalwhere

The Genetic Algorithms (GAs)The Genetic Algorithms (GAs) The evolution process for generating a populationThe evolution process for generating a population

Father

Mother

Mutation

SelectionMating

Child 1

Next Generation

Crossover

Initial Generation

N

Chromosome

Gene

One-flip mutation in child 2

Child 2

One- point crossover

Neural Networks NNNeural Networks NN imitate the human brain imitate the human brain

NN consists of a collection of interconnected simple computational units that work together cooperatively (like synapses in the human brain) These units are input units for receiving information from the problem domain hidden units as internal weight structure and output units for broadcasting data

Different types of the GPS Surveying Different types of the GPS Surveying NetworksNetworks

The Seychelles GPS Network (linear) The Malta GPS Network (triangulation)

Handicapped Person Transportation in the City of Handicapped Person Transportation in the City of Bruxelles using Grouping Genetic AlgorithmBruxelles using Grouping Genetic Algorithm

D1

D2

(n)

O2

O1

(m)

D

O

Depot1

Depot2

The covered area by Depot2The covered area by Depot 1

0

2

3 1

4

LocationDirection of vehicleContour of networkRegion of massif influx

[Ostarti =1000 Oendi =1015] [Dstarti =1100 Dendi =1130]

Desired departure interval=15 mins Desired arrival interval=30 mins

Travel time=0060 mins [1010 -1110]

1110 1010

Oi (home) Di (hospital)

Disaster Warning Network for Danube Disaster Warning Network for Danube Basin (DWN)Basin (DWN)

bull DWN is a system of satellites and ground stations for providing real time early warning of the impact of a disaster

bull DWN implements a network of Control Stations (CSs) spread over the whole geographic area of the hazard and transmits their observations to a master station

bull DWN provides this reliable information on a continuous basis through the parallel process of coverage accuracy prediction and integrity risk simulation functions

Disaster Warning Network Disaster Warning Network for the Danube Basinfor the Danube Basin

Reference Receiver RR

Roving Receiver 2

Roving Receiver 3

Roving Receiver 1

SatelliteGPS Signal 1

Error correction message 1

Roving Receiver 4

GPS Signal 4

GPS Signal RR

GPS Signal 2

GPS Signal 3

Error correction message 4

Error correction message 2

Error correction message 3

Differential GPSDifferential GPS

The DWN model for Environmental The DWN model for Environmental Pollution ControlPollution Control

bull Multiobjective Optimisation Problems (MOPs)

bull f(x) is the objective function to be optimised and X is the search domain

bull Ps is a multiplier for measuring the size of the user population near the location s (eg safety teams)bull f(acy ing) are the evaluations of accuracy coverage and integrity performance respectively for user location

sbull f(cost) is the cost function of a given DWN designbull N is the number of user locationsbull n is the number of obtained DWN designs

Xxxxxtosubject

xfxfxfxfoptimizeMOP

n

n

21

21

CostfffPnF n

N

s

sing

sacysDWN

1

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

bull Dynamic Metahuristics Algorithms (DMA) potentially have these capabilities to produce set of high quality real-time designs that can model more closely and easily the objective functions and tolerate the noisy evaluations given by these functions

bull The final design should be robust (ie performs well over a wide range of environment conditions) and flexible (ie allows easy adaptation after the environment has changed)

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

bull DMAs can successfully handle a mix of continuous and discrete parameters as well as selecting individual components from database Therefore another objective of this project is to connect DWN to a database which includes all the related information (physical geographicaland biochemical) colleted by other observation techniques (eg Geographic Information System (GIS) Remote Sensing (RS) photogrametery internet air (water and soil) survey technology etc)

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

DWN can effectively optimise this problem by 1) providing access to a wide range of data types in real-time 2) combining the observational data with innovative data analysis to improve forecasting and risk assessment 3) and being a user friendly tool in decision support of environmental management

Current Applications (Earthquake) (1)Current Applications (Earthquake) (1)

bull GEO-INFORMATION TECHNOLOGY AND ARTIFICIAL INTELLIGENCE FOR OPTIMIZING THE DISPLACEMENTS OF EARTHQUAKES A CASE STUDY FOR SYRIAN ACTIVE FAULT ZONES

bull With Dr Mohamad Rukieh Syrian Ministry of Commuincation and Technology

bull International Union of Geodesy and Geophysics IUGG07bull Perugia Italy July 3-14 2007bull httpwwwiugg2007perugiait

Distribution to the UsersDistribution to the Users

Data collection

SpaceAirborne ground

Ancillary

Dynamic Data

Processor

Main Services (File Spatial Data Engine Internet Map Web etc)

Desktop GIS Tools

InternetIPhttp

Information Flow amp Early WarningInformation Flow amp Early WarningSystem Architecture in Flood ManagementSystem Architecture in Flood Management

Risk Assessmen

t

Early Warning

Monitoring

Damage Assessmen

t

Relief provision

Web site bulletin e-mail fax radio telephone etc

Dissemination

Hydrological Stations Provision of Forecasts

Flood Management amp Risk Reduction (FMRR)Flood Management amp Risk Reduction (FMRR)

1 Regional FMRR 1 Regional FMRR centrecentre

5 Land Use management5 Land Use management

4 Flood Emergency Mngmt4 Flood Emergency Mngmt

3 Trans-boundary mediation3 Trans-boundary mediation

2 Structural Measures amp2 Structural Measures ampFlood ProofingFlood Proofing

Capacity BuildingCapacity Building(Training Unit)(Training Unit)

Data Collection Data Collection amp Processingamp Processing

Forecasting Forecasting Warning amp Warning amp DisseminationDissemination

Annual Flood Forum Annual Flood Forum Workshops Workshops CommunicationsCommunications

FMRRFMRR

6 Spatial Planning6 Spatial Planning

Disaster reduction as part of sustainable development through1048707 Strengthen institutions (especially in communities) to build resilience1048707 Build risk reduction into emergency management and recovery

Remote Sensing Technology

IRS-P6 AWiFS Image of 21-Dec-04 IRS-P6 AWiFS Image of 26-Dec-04

A Close View of Camorta Island Tsunami - 2004

Pre-Event Post-Event

Global Navigation Satellite Systems GNSSsGlobal Navigation Satellite Systems GNSSs

GALILILEO

Sat2

Receiver

X

Z

Y(0 0 0)

WGS-84

Local

Sat3

Sat1

Sat4

GPS GLONASS

Geographic Information ProcessGeographic Information Process Database amp VisualizationDatabase amp Visualization

The geographic information process consists of three stages Data acquisition data processing and data dissemination

Database ldquoNot easy to interpretrdquo

Visualisation ldquoWorth a Thousand wordsrdquo

Rules of Spatial Planning Rules of Spatial Planning

in Flood Managementin Flood Management flood reduction is part of sustainable development and the various action possibilities

to improve preventive flood protection based on spatial planning and urban development can be described as follow

bull Protection of existing retention areas eg declaration of flood areas etcbull Extension of retention areas eg creating detention ponds etcbull Retention in the catchments eg restoration of small streams etcbull Minimisation of damage potential eg preventive land-use management etcbull Technical flood protection measures eg dikes retention ponds etc

As shown from above flood risk can only effectively be reduced if in addition to the technical measures spatial planning regulates land-use in flood-prone areas

Flood Forecasting amp Early Warning

Provide accurate Provide accurate forecasts with a forecasts with a suitable lead time suitable lead time and a timely and and a timely and effective effective disseminationdissemination

Heavy rainsnowmelt or high inflow

Data collection and transmission

Boundary estimation (rainfall tide )

Forecast calculation

Warning dissemination

Emergency action

Flooding starts

time

Meteorological forecast

Min

imis

em

axim

ise

Multiobjective Combinatorial Optimisation Multiobjective Combinatorial Optimisation Problems (MCOPs)Problems (MCOPs)

Many real-life applications involves two types of problem difficulty 1) multiple conflicting objectives 2) a highly complex search domain

Xnx2x1xxtosubjectxnfx2fx1fxfoptimizeOPCM

X is the search domain fi(x) is the ith objective function to be optimised

x is a set of decision vectors

Minimize cost

Question How do I combine all the fieldwork components and find the optimal schedule for receivers Therefore we need to answer not only the question

ldquo What is the best rdquo but also ldquoWhat is sufficiently robustrdquoAnswer Metaheuristic Techniques

MaximisationMaximisation(Global and local maxima)(Global and local maxima) Objective function

Iteration

Global maximum

Local maximum

In mechanical engineering an engineer wishes to design a car consisting of composite materials The engineer by optimization (maximization in this case) will conceivably design a lighter stronger attractive and safer composite car

In GPS surveying a surveyor wants to design a network taking in consideration components of the field-work The GPS surveyor by optimisation (minimization in this case) will design a cheaper and more acceptable schedule

Objective function

Iteration

Global minimum

Local minimum

MinimizationMinimization(Global and local minima)(Global and local minima)

Schematic Representation of the Search Schematic Representation of the Search Progress of Metaheuristic TechniquesProgress of Metaheuristic Techniques

S0

S2

S3

S4

S5

S6

S7

S8

S9

S10

S11

S12I(S0)

I(S4) I(S9)

I(S6)I(S12)

Q(S)

S1

Iterations

Values of Si

or the objective function

Select a given solution S I(S) and compute its value C(S) Generate a schedule Srsquo I(S) and compute its value C(Srsquo)If C(Srsquo) lt C(S) then Srsquo replaces S as a current solutionOtherwise retain S and generate other moves until C(Srsquo)ltC(S) for all Srsquo I(S)Terminate the search and return S as the local optimal solution

Q(S)I(S0)

S0

Metaheuristic TechniquesMetaheuristic Techniques bull Metaheuristic technique is an iterative self-learning procedure for

quickly and efficiently identifying a high quality solution for COPs

bull Types of Metaheuristics 1- Solution improvement techniques Simulated Annealing SA Tabu Search

TS 2- Solution construction techniques Ant Colony Optimisation ACO Genetic

Algorithm GA Neural Network NNbull Fundamental concepts of the

metaheuristics 1- Selection or construction of the initial

solution 2- Generation of the neighbouring solutions 3- Acceptance of solutions 4- Stopping criteria

Initial Solution Formation

Neighbourhood

Search by Move Formation(Local Search)

Provisional Neighbourhood Formation

Search by Solution Formation(Development and guided search)

Neighbourhood for the nextsolution formation

Acceptance Criteria

Termination of the Search

The Simulated Annealing (SA) TechniqueThe Simulated Annealing (SA) Technique

Crystalline solid (frozen) state (T=0) Amorphous solid (frozen) state (T=0)

Ucirc High free energy (high temperature)

The material as a system of particles(Cooling process)

Annealing (slow cooling) Quenching (fast cooling)

Ucirc Ucirc Global minimum of the energy Local minimum of the energy

Case II Case III

Case I

bull Based on analogy between combinatorial optimization and annealing process of solids

bull Improvement of solution for move from S to Srsquo always accepted

bull Accepts uphill move only with given probability (decreases in a number of rounds to zero)

bull Cooling parameters the initial starting value of the temperature the temperature length (Markov Chain) L the cooling ratio F

Temperature

Iterations

Stop

StartK

F

LTi

Tf

The Tabu Search (TS) techniqueThe Tabu Search (TS) technique

bull Add memory to LS (Prevent Cycling increase diversity of exploration encourage escape from local optima)

bull Avoids cycling (visiting same solution more than once) by use of short term memory for the very recent history and long-term memory for the distant history

bull TS parameters Size of Memry Contents of memory (solution based attributes eg changes to solution features) long term memory

bull Tabu Listbull Candidate Listbull Tabu Tenure

The modified ACO will have some major differences with a real (natural) one

bull Artificial ants will have some memorybull Artificial ants will not be completely blindbull Artificial ants will live in an environment

where time is discrete

The Ant Colony Optimization The Ant Colony Optimization ACOACO

ACOACO

We will finish on time Do not worry OK

bull Construction the Initial Schedule

bull Local Searchbull Local Updating Rule

bull Global Updating Rule

otherwise0

(r)Jsifrηrτsrηsrτ

s)(rPk

(r)Ju

β

β

kk

uu

srΔτρsrτρ1srτ

otherwise0

srifLs)(rΔτ

srΔταsrτα1srτ1

gb tourbestglobalwhere

The Genetic Algorithms (GAs)The Genetic Algorithms (GAs) The evolution process for generating a populationThe evolution process for generating a population

Father

Mother

Mutation

SelectionMating

Child 1

Next Generation

Crossover

Initial Generation

N

Chromosome

Gene

One-flip mutation in child 2

Child 2

One- point crossover

Neural Networks NNNeural Networks NN imitate the human brain imitate the human brain

NN consists of a collection of interconnected simple computational units that work together cooperatively (like synapses in the human brain) These units are input units for receiving information from the problem domain hidden units as internal weight structure and output units for broadcasting data

Different types of the GPS Surveying Different types of the GPS Surveying NetworksNetworks

The Seychelles GPS Network (linear) The Malta GPS Network (triangulation)

Handicapped Person Transportation in the City of Handicapped Person Transportation in the City of Bruxelles using Grouping Genetic AlgorithmBruxelles using Grouping Genetic Algorithm

D1

D2

(n)

O2

O1

(m)

D

O

Depot1

Depot2

The covered area by Depot2The covered area by Depot 1

0

2

3 1

4

LocationDirection of vehicleContour of networkRegion of massif influx

[Ostarti =1000 Oendi =1015] [Dstarti =1100 Dendi =1130]

Desired departure interval=15 mins Desired arrival interval=30 mins

Travel time=0060 mins [1010 -1110]

1110 1010

Oi (home) Di (hospital)

Disaster Warning Network for Danube Disaster Warning Network for Danube Basin (DWN)Basin (DWN)

bull DWN is a system of satellites and ground stations for providing real time early warning of the impact of a disaster

bull DWN implements a network of Control Stations (CSs) spread over the whole geographic area of the hazard and transmits their observations to a master station

bull DWN provides this reliable information on a continuous basis through the parallel process of coverage accuracy prediction and integrity risk simulation functions

Disaster Warning Network Disaster Warning Network for the Danube Basinfor the Danube Basin

Reference Receiver RR

Roving Receiver 2

Roving Receiver 3

Roving Receiver 1

SatelliteGPS Signal 1

Error correction message 1

Roving Receiver 4

GPS Signal 4

GPS Signal RR

GPS Signal 2

GPS Signal 3

Error correction message 4

Error correction message 2

Error correction message 3

Differential GPSDifferential GPS

The DWN model for Environmental The DWN model for Environmental Pollution ControlPollution Control

bull Multiobjective Optimisation Problems (MOPs)

bull f(x) is the objective function to be optimised and X is the search domain

bull Ps is a multiplier for measuring the size of the user population near the location s (eg safety teams)bull f(acy ing) are the evaluations of accuracy coverage and integrity performance respectively for user location

sbull f(cost) is the cost function of a given DWN designbull N is the number of user locationsbull n is the number of obtained DWN designs

Xxxxxtosubject

xfxfxfxfoptimizeMOP

n

n

21

21

CostfffPnF n

N

s

sing

sacysDWN

1

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

bull Dynamic Metahuristics Algorithms (DMA) potentially have these capabilities to produce set of high quality real-time designs that can model more closely and easily the objective functions and tolerate the noisy evaluations given by these functions

bull The final design should be robust (ie performs well over a wide range of environment conditions) and flexible (ie allows easy adaptation after the environment has changed)

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

bull DMAs can successfully handle a mix of continuous and discrete parameters as well as selecting individual components from database Therefore another objective of this project is to connect DWN to a database which includes all the related information (physical geographicaland biochemical) colleted by other observation techniques (eg Geographic Information System (GIS) Remote Sensing (RS) photogrametery internet air (water and soil) survey technology etc)

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

DWN can effectively optimise this problem by 1) providing access to a wide range of data types in real-time 2) combining the observational data with innovative data analysis to improve forecasting and risk assessment 3) and being a user friendly tool in decision support of environmental management

Current Applications (Earthquake) (1)Current Applications (Earthquake) (1)

bull GEO-INFORMATION TECHNOLOGY AND ARTIFICIAL INTELLIGENCE FOR OPTIMIZING THE DISPLACEMENTS OF EARTHQUAKES A CASE STUDY FOR SYRIAN ACTIVE FAULT ZONES

bull With Dr Mohamad Rukieh Syrian Ministry of Commuincation and Technology

bull International Union of Geodesy and Geophysics IUGG07bull Perugia Italy July 3-14 2007bull httpwwwiugg2007perugiait

Flood Management amp Risk Reduction (FMRR)Flood Management amp Risk Reduction (FMRR)

1 Regional FMRR 1 Regional FMRR centrecentre

5 Land Use management5 Land Use management

4 Flood Emergency Mngmt4 Flood Emergency Mngmt

3 Trans-boundary mediation3 Trans-boundary mediation

2 Structural Measures amp2 Structural Measures ampFlood ProofingFlood Proofing

Capacity BuildingCapacity Building(Training Unit)(Training Unit)

Data Collection Data Collection amp Processingamp Processing

Forecasting Forecasting Warning amp Warning amp DisseminationDissemination

Annual Flood Forum Annual Flood Forum Workshops Workshops CommunicationsCommunications

FMRRFMRR

6 Spatial Planning6 Spatial Planning

Disaster reduction as part of sustainable development through1048707 Strengthen institutions (especially in communities) to build resilience1048707 Build risk reduction into emergency management and recovery

Remote Sensing Technology

IRS-P6 AWiFS Image of 21-Dec-04 IRS-P6 AWiFS Image of 26-Dec-04

A Close View of Camorta Island Tsunami - 2004

Pre-Event Post-Event

Global Navigation Satellite Systems GNSSsGlobal Navigation Satellite Systems GNSSs

GALILILEO

Sat2

Receiver

X

Z

Y(0 0 0)

WGS-84

Local

Sat3

Sat1

Sat4

GPS GLONASS

Geographic Information ProcessGeographic Information Process Database amp VisualizationDatabase amp Visualization

The geographic information process consists of three stages Data acquisition data processing and data dissemination

Database ldquoNot easy to interpretrdquo

Visualisation ldquoWorth a Thousand wordsrdquo

Rules of Spatial Planning Rules of Spatial Planning

in Flood Managementin Flood Management flood reduction is part of sustainable development and the various action possibilities

to improve preventive flood protection based on spatial planning and urban development can be described as follow

bull Protection of existing retention areas eg declaration of flood areas etcbull Extension of retention areas eg creating detention ponds etcbull Retention in the catchments eg restoration of small streams etcbull Minimisation of damage potential eg preventive land-use management etcbull Technical flood protection measures eg dikes retention ponds etc

As shown from above flood risk can only effectively be reduced if in addition to the technical measures spatial planning regulates land-use in flood-prone areas

Flood Forecasting amp Early Warning

Provide accurate Provide accurate forecasts with a forecasts with a suitable lead time suitable lead time and a timely and and a timely and effective effective disseminationdissemination

Heavy rainsnowmelt or high inflow

Data collection and transmission

Boundary estimation (rainfall tide )

Forecast calculation

Warning dissemination

Emergency action

Flooding starts

time

Meteorological forecast

Min

imis

em

axim

ise

Multiobjective Combinatorial Optimisation Multiobjective Combinatorial Optimisation Problems (MCOPs)Problems (MCOPs)

Many real-life applications involves two types of problem difficulty 1) multiple conflicting objectives 2) a highly complex search domain

Xnx2x1xxtosubjectxnfx2fx1fxfoptimizeOPCM

X is the search domain fi(x) is the ith objective function to be optimised

x is a set of decision vectors

Minimize cost

Question How do I combine all the fieldwork components and find the optimal schedule for receivers Therefore we need to answer not only the question

ldquo What is the best rdquo but also ldquoWhat is sufficiently robustrdquoAnswer Metaheuristic Techniques

MaximisationMaximisation(Global and local maxima)(Global and local maxima) Objective function

Iteration

Global maximum

Local maximum

In mechanical engineering an engineer wishes to design a car consisting of composite materials The engineer by optimization (maximization in this case) will conceivably design a lighter stronger attractive and safer composite car

In GPS surveying a surveyor wants to design a network taking in consideration components of the field-work The GPS surveyor by optimisation (minimization in this case) will design a cheaper and more acceptable schedule

Objective function

Iteration

Global minimum

Local minimum

MinimizationMinimization(Global and local minima)(Global and local minima)

Schematic Representation of the Search Schematic Representation of the Search Progress of Metaheuristic TechniquesProgress of Metaheuristic Techniques

S0

S2

S3

S4

S5

S6

S7

S8

S9

S10

S11

S12I(S0)

I(S4) I(S9)

I(S6)I(S12)

Q(S)

S1

Iterations

Values of Si

or the objective function

Select a given solution S I(S) and compute its value C(S) Generate a schedule Srsquo I(S) and compute its value C(Srsquo)If C(Srsquo) lt C(S) then Srsquo replaces S as a current solutionOtherwise retain S and generate other moves until C(Srsquo)ltC(S) for all Srsquo I(S)Terminate the search and return S as the local optimal solution

Q(S)I(S0)

S0

Metaheuristic TechniquesMetaheuristic Techniques bull Metaheuristic technique is an iterative self-learning procedure for

quickly and efficiently identifying a high quality solution for COPs

bull Types of Metaheuristics 1- Solution improvement techniques Simulated Annealing SA Tabu Search

TS 2- Solution construction techniques Ant Colony Optimisation ACO Genetic

Algorithm GA Neural Network NNbull Fundamental concepts of the

metaheuristics 1- Selection or construction of the initial

solution 2- Generation of the neighbouring solutions 3- Acceptance of solutions 4- Stopping criteria

Initial Solution Formation

Neighbourhood

Search by Move Formation(Local Search)

Provisional Neighbourhood Formation

Search by Solution Formation(Development and guided search)

Neighbourhood for the nextsolution formation

Acceptance Criteria

Termination of the Search

The Simulated Annealing (SA) TechniqueThe Simulated Annealing (SA) Technique

Crystalline solid (frozen) state (T=0) Amorphous solid (frozen) state (T=0)

Ucirc High free energy (high temperature)

The material as a system of particles(Cooling process)

Annealing (slow cooling) Quenching (fast cooling)

Ucirc Ucirc Global minimum of the energy Local minimum of the energy

Case II Case III

Case I

bull Based on analogy between combinatorial optimization and annealing process of solids

bull Improvement of solution for move from S to Srsquo always accepted

bull Accepts uphill move only with given probability (decreases in a number of rounds to zero)

bull Cooling parameters the initial starting value of the temperature the temperature length (Markov Chain) L the cooling ratio F

Temperature

Iterations

Stop

StartK

F

LTi

Tf

The Tabu Search (TS) techniqueThe Tabu Search (TS) technique

bull Add memory to LS (Prevent Cycling increase diversity of exploration encourage escape from local optima)

bull Avoids cycling (visiting same solution more than once) by use of short term memory for the very recent history and long-term memory for the distant history

bull TS parameters Size of Memry Contents of memory (solution based attributes eg changes to solution features) long term memory

bull Tabu Listbull Candidate Listbull Tabu Tenure

The modified ACO will have some major differences with a real (natural) one

bull Artificial ants will have some memorybull Artificial ants will not be completely blindbull Artificial ants will live in an environment

where time is discrete

The Ant Colony Optimization The Ant Colony Optimization ACOACO

ACOACO

We will finish on time Do not worry OK

bull Construction the Initial Schedule

bull Local Searchbull Local Updating Rule

bull Global Updating Rule

otherwise0

(r)Jsifrηrτsrηsrτ

s)(rPk

(r)Ju

β

β

kk

uu

srΔτρsrτρ1srτ

otherwise0

srifLs)(rΔτ

srΔταsrτα1srτ1

gb tourbestglobalwhere

The Genetic Algorithms (GAs)The Genetic Algorithms (GAs) The evolution process for generating a populationThe evolution process for generating a population

Father

Mother

Mutation

SelectionMating

Child 1

Next Generation

Crossover

Initial Generation

N

Chromosome

Gene

One-flip mutation in child 2

Child 2

One- point crossover

Neural Networks NNNeural Networks NN imitate the human brain imitate the human brain

NN consists of a collection of interconnected simple computational units that work together cooperatively (like synapses in the human brain) These units are input units for receiving information from the problem domain hidden units as internal weight structure and output units for broadcasting data

Different types of the GPS Surveying Different types of the GPS Surveying NetworksNetworks

The Seychelles GPS Network (linear) The Malta GPS Network (triangulation)

Handicapped Person Transportation in the City of Handicapped Person Transportation in the City of Bruxelles using Grouping Genetic AlgorithmBruxelles using Grouping Genetic Algorithm

D1

D2

(n)

O2

O1

(m)

D

O

Depot1

Depot2

The covered area by Depot2The covered area by Depot 1

0

2

3 1

4

LocationDirection of vehicleContour of networkRegion of massif influx

[Ostarti =1000 Oendi =1015] [Dstarti =1100 Dendi =1130]

Desired departure interval=15 mins Desired arrival interval=30 mins

Travel time=0060 mins [1010 -1110]

1110 1010

Oi (home) Di (hospital)

Disaster Warning Network for Danube Disaster Warning Network for Danube Basin (DWN)Basin (DWN)

bull DWN is a system of satellites and ground stations for providing real time early warning of the impact of a disaster

bull DWN implements a network of Control Stations (CSs) spread over the whole geographic area of the hazard and transmits their observations to a master station

bull DWN provides this reliable information on a continuous basis through the parallel process of coverage accuracy prediction and integrity risk simulation functions

Disaster Warning Network Disaster Warning Network for the Danube Basinfor the Danube Basin

Reference Receiver RR

Roving Receiver 2

Roving Receiver 3

Roving Receiver 1

SatelliteGPS Signal 1

Error correction message 1

Roving Receiver 4

GPS Signal 4

GPS Signal RR

GPS Signal 2

GPS Signal 3

Error correction message 4

Error correction message 2

Error correction message 3

Differential GPSDifferential GPS

The DWN model for Environmental The DWN model for Environmental Pollution ControlPollution Control

bull Multiobjective Optimisation Problems (MOPs)

bull f(x) is the objective function to be optimised and X is the search domain

bull Ps is a multiplier for measuring the size of the user population near the location s (eg safety teams)bull f(acy ing) are the evaluations of accuracy coverage and integrity performance respectively for user location

sbull f(cost) is the cost function of a given DWN designbull N is the number of user locationsbull n is the number of obtained DWN designs

Xxxxxtosubject

xfxfxfxfoptimizeMOP

n

n

21

21

CostfffPnF n

N

s

sing

sacysDWN

1

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

bull Dynamic Metahuristics Algorithms (DMA) potentially have these capabilities to produce set of high quality real-time designs that can model more closely and easily the objective functions and tolerate the noisy evaluations given by these functions

bull The final design should be robust (ie performs well over a wide range of environment conditions) and flexible (ie allows easy adaptation after the environment has changed)

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

bull DMAs can successfully handle a mix of continuous and discrete parameters as well as selecting individual components from database Therefore another objective of this project is to connect DWN to a database which includes all the related information (physical geographicaland biochemical) colleted by other observation techniques (eg Geographic Information System (GIS) Remote Sensing (RS) photogrametery internet air (water and soil) survey technology etc)

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

DWN can effectively optimise this problem by 1) providing access to a wide range of data types in real-time 2) combining the observational data with innovative data analysis to improve forecasting and risk assessment 3) and being a user friendly tool in decision support of environmental management

Current Applications (Earthquake) (1)Current Applications (Earthquake) (1)

bull GEO-INFORMATION TECHNOLOGY AND ARTIFICIAL INTELLIGENCE FOR OPTIMIZING THE DISPLACEMENTS OF EARTHQUAKES A CASE STUDY FOR SYRIAN ACTIVE FAULT ZONES

bull With Dr Mohamad Rukieh Syrian Ministry of Commuincation and Technology

bull International Union of Geodesy and Geophysics IUGG07bull Perugia Italy July 3-14 2007bull httpwwwiugg2007perugiait

Remote Sensing Technology

IRS-P6 AWiFS Image of 21-Dec-04 IRS-P6 AWiFS Image of 26-Dec-04

A Close View of Camorta Island Tsunami - 2004

Pre-Event Post-Event

Global Navigation Satellite Systems GNSSsGlobal Navigation Satellite Systems GNSSs

GALILILEO

Sat2

Receiver

X

Z

Y(0 0 0)

WGS-84

Local

Sat3

Sat1

Sat4

GPS GLONASS

Geographic Information ProcessGeographic Information Process Database amp VisualizationDatabase amp Visualization

The geographic information process consists of three stages Data acquisition data processing and data dissemination

Database ldquoNot easy to interpretrdquo

Visualisation ldquoWorth a Thousand wordsrdquo

Rules of Spatial Planning Rules of Spatial Planning

in Flood Managementin Flood Management flood reduction is part of sustainable development and the various action possibilities

to improve preventive flood protection based on spatial planning and urban development can be described as follow

bull Protection of existing retention areas eg declaration of flood areas etcbull Extension of retention areas eg creating detention ponds etcbull Retention in the catchments eg restoration of small streams etcbull Minimisation of damage potential eg preventive land-use management etcbull Technical flood protection measures eg dikes retention ponds etc

As shown from above flood risk can only effectively be reduced if in addition to the technical measures spatial planning regulates land-use in flood-prone areas

Flood Forecasting amp Early Warning

Provide accurate Provide accurate forecasts with a forecasts with a suitable lead time suitable lead time and a timely and and a timely and effective effective disseminationdissemination

Heavy rainsnowmelt or high inflow

Data collection and transmission

Boundary estimation (rainfall tide )

Forecast calculation

Warning dissemination

Emergency action

Flooding starts

time

Meteorological forecast

Min

imis

em

axim

ise

Multiobjective Combinatorial Optimisation Multiobjective Combinatorial Optimisation Problems (MCOPs)Problems (MCOPs)

Many real-life applications involves two types of problem difficulty 1) multiple conflicting objectives 2) a highly complex search domain

Xnx2x1xxtosubjectxnfx2fx1fxfoptimizeOPCM

X is the search domain fi(x) is the ith objective function to be optimised

x is a set of decision vectors

Minimize cost

Question How do I combine all the fieldwork components and find the optimal schedule for receivers Therefore we need to answer not only the question

ldquo What is the best rdquo but also ldquoWhat is sufficiently robustrdquoAnswer Metaheuristic Techniques

MaximisationMaximisation(Global and local maxima)(Global and local maxima) Objective function

Iteration

Global maximum

Local maximum

In mechanical engineering an engineer wishes to design a car consisting of composite materials The engineer by optimization (maximization in this case) will conceivably design a lighter stronger attractive and safer composite car

In GPS surveying a surveyor wants to design a network taking in consideration components of the field-work The GPS surveyor by optimisation (minimization in this case) will design a cheaper and more acceptable schedule

Objective function

Iteration

Global minimum

Local minimum

MinimizationMinimization(Global and local minima)(Global and local minima)

Schematic Representation of the Search Schematic Representation of the Search Progress of Metaheuristic TechniquesProgress of Metaheuristic Techniques

S0

S2

S3

S4

S5

S6

S7

S8

S9

S10

S11

S12I(S0)

I(S4) I(S9)

I(S6)I(S12)

Q(S)

S1

Iterations

Values of Si

or the objective function

Select a given solution S I(S) and compute its value C(S) Generate a schedule Srsquo I(S) and compute its value C(Srsquo)If C(Srsquo) lt C(S) then Srsquo replaces S as a current solutionOtherwise retain S and generate other moves until C(Srsquo)ltC(S) for all Srsquo I(S)Terminate the search and return S as the local optimal solution

Q(S)I(S0)

S0

Metaheuristic TechniquesMetaheuristic Techniques bull Metaheuristic technique is an iterative self-learning procedure for

quickly and efficiently identifying a high quality solution for COPs

bull Types of Metaheuristics 1- Solution improvement techniques Simulated Annealing SA Tabu Search

TS 2- Solution construction techniques Ant Colony Optimisation ACO Genetic

Algorithm GA Neural Network NNbull Fundamental concepts of the

metaheuristics 1- Selection or construction of the initial

solution 2- Generation of the neighbouring solutions 3- Acceptance of solutions 4- Stopping criteria

Initial Solution Formation

Neighbourhood

Search by Move Formation(Local Search)

Provisional Neighbourhood Formation

Search by Solution Formation(Development and guided search)

Neighbourhood for the nextsolution formation

Acceptance Criteria

Termination of the Search

The Simulated Annealing (SA) TechniqueThe Simulated Annealing (SA) Technique

Crystalline solid (frozen) state (T=0) Amorphous solid (frozen) state (T=0)

Ucirc High free energy (high temperature)

The material as a system of particles(Cooling process)

Annealing (slow cooling) Quenching (fast cooling)

Ucirc Ucirc Global minimum of the energy Local minimum of the energy

Case II Case III

Case I

bull Based on analogy between combinatorial optimization and annealing process of solids

bull Improvement of solution for move from S to Srsquo always accepted

bull Accepts uphill move only with given probability (decreases in a number of rounds to zero)

bull Cooling parameters the initial starting value of the temperature the temperature length (Markov Chain) L the cooling ratio F

Temperature

Iterations

Stop

StartK

F

LTi

Tf

The Tabu Search (TS) techniqueThe Tabu Search (TS) technique

bull Add memory to LS (Prevent Cycling increase diversity of exploration encourage escape from local optima)

bull Avoids cycling (visiting same solution more than once) by use of short term memory for the very recent history and long-term memory for the distant history

bull TS parameters Size of Memry Contents of memory (solution based attributes eg changes to solution features) long term memory

bull Tabu Listbull Candidate Listbull Tabu Tenure

The modified ACO will have some major differences with a real (natural) one

bull Artificial ants will have some memorybull Artificial ants will not be completely blindbull Artificial ants will live in an environment

where time is discrete

The Ant Colony Optimization The Ant Colony Optimization ACOACO

ACOACO

We will finish on time Do not worry OK

bull Construction the Initial Schedule

bull Local Searchbull Local Updating Rule

bull Global Updating Rule

otherwise0

(r)Jsifrηrτsrηsrτ

s)(rPk

(r)Ju

β

β

kk

uu

srΔτρsrτρ1srτ

otherwise0

srifLs)(rΔτ

srΔταsrτα1srτ1

gb tourbestglobalwhere

The Genetic Algorithms (GAs)The Genetic Algorithms (GAs) The evolution process for generating a populationThe evolution process for generating a population

Father

Mother

Mutation

SelectionMating

Child 1

Next Generation

Crossover

Initial Generation

N

Chromosome

Gene

One-flip mutation in child 2

Child 2

One- point crossover

Neural Networks NNNeural Networks NN imitate the human brain imitate the human brain

NN consists of a collection of interconnected simple computational units that work together cooperatively (like synapses in the human brain) These units are input units for receiving information from the problem domain hidden units as internal weight structure and output units for broadcasting data

Different types of the GPS Surveying Different types of the GPS Surveying NetworksNetworks

The Seychelles GPS Network (linear) The Malta GPS Network (triangulation)

Handicapped Person Transportation in the City of Handicapped Person Transportation in the City of Bruxelles using Grouping Genetic AlgorithmBruxelles using Grouping Genetic Algorithm

D1

D2

(n)

O2

O1

(m)

D

O

Depot1

Depot2

The covered area by Depot2The covered area by Depot 1

0

2

3 1

4

LocationDirection of vehicleContour of networkRegion of massif influx

[Ostarti =1000 Oendi =1015] [Dstarti =1100 Dendi =1130]

Desired departure interval=15 mins Desired arrival interval=30 mins

Travel time=0060 mins [1010 -1110]

1110 1010

Oi (home) Di (hospital)

Disaster Warning Network for Danube Disaster Warning Network for Danube Basin (DWN)Basin (DWN)

bull DWN is a system of satellites and ground stations for providing real time early warning of the impact of a disaster

bull DWN implements a network of Control Stations (CSs) spread over the whole geographic area of the hazard and transmits their observations to a master station

bull DWN provides this reliable information on a continuous basis through the parallel process of coverage accuracy prediction and integrity risk simulation functions

Disaster Warning Network Disaster Warning Network for the Danube Basinfor the Danube Basin

Reference Receiver RR

Roving Receiver 2

Roving Receiver 3

Roving Receiver 1

SatelliteGPS Signal 1

Error correction message 1

Roving Receiver 4

GPS Signal 4

GPS Signal RR

GPS Signal 2

GPS Signal 3

Error correction message 4

Error correction message 2

Error correction message 3

Differential GPSDifferential GPS

The DWN model for Environmental The DWN model for Environmental Pollution ControlPollution Control

bull Multiobjective Optimisation Problems (MOPs)

bull f(x) is the objective function to be optimised and X is the search domain

bull Ps is a multiplier for measuring the size of the user population near the location s (eg safety teams)bull f(acy ing) are the evaluations of accuracy coverage and integrity performance respectively for user location

sbull f(cost) is the cost function of a given DWN designbull N is the number of user locationsbull n is the number of obtained DWN designs

Xxxxxtosubject

xfxfxfxfoptimizeMOP

n

n

21

21

CostfffPnF n

N

s

sing

sacysDWN

1

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

bull Dynamic Metahuristics Algorithms (DMA) potentially have these capabilities to produce set of high quality real-time designs that can model more closely and easily the objective functions and tolerate the noisy evaluations given by these functions

bull The final design should be robust (ie performs well over a wide range of environment conditions) and flexible (ie allows easy adaptation after the environment has changed)

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

bull DMAs can successfully handle a mix of continuous and discrete parameters as well as selecting individual components from database Therefore another objective of this project is to connect DWN to a database which includes all the related information (physical geographicaland biochemical) colleted by other observation techniques (eg Geographic Information System (GIS) Remote Sensing (RS) photogrametery internet air (water and soil) survey technology etc)

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

DWN can effectively optimise this problem by 1) providing access to a wide range of data types in real-time 2) combining the observational data with innovative data analysis to improve forecasting and risk assessment 3) and being a user friendly tool in decision support of environmental management

Current Applications (Earthquake) (1)Current Applications (Earthquake) (1)

bull GEO-INFORMATION TECHNOLOGY AND ARTIFICIAL INTELLIGENCE FOR OPTIMIZING THE DISPLACEMENTS OF EARTHQUAKES A CASE STUDY FOR SYRIAN ACTIVE FAULT ZONES

bull With Dr Mohamad Rukieh Syrian Ministry of Commuincation and Technology

bull International Union of Geodesy and Geophysics IUGG07bull Perugia Italy July 3-14 2007bull httpwwwiugg2007perugiait

Global Navigation Satellite Systems GNSSsGlobal Navigation Satellite Systems GNSSs

GALILILEO

Sat2

Receiver

X

Z

Y(0 0 0)

WGS-84

Local

Sat3

Sat1

Sat4

GPS GLONASS

Geographic Information ProcessGeographic Information Process Database amp VisualizationDatabase amp Visualization

The geographic information process consists of three stages Data acquisition data processing and data dissemination

Database ldquoNot easy to interpretrdquo

Visualisation ldquoWorth a Thousand wordsrdquo

Rules of Spatial Planning Rules of Spatial Planning

in Flood Managementin Flood Management flood reduction is part of sustainable development and the various action possibilities

to improve preventive flood protection based on spatial planning and urban development can be described as follow

bull Protection of existing retention areas eg declaration of flood areas etcbull Extension of retention areas eg creating detention ponds etcbull Retention in the catchments eg restoration of small streams etcbull Minimisation of damage potential eg preventive land-use management etcbull Technical flood protection measures eg dikes retention ponds etc

As shown from above flood risk can only effectively be reduced if in addition to the technical measures spatial planning regulates land-use in flood-prone areas

Flood Forecasting amp Early Warning

Provide accurate Provide accurate forecasts with a forecasts with a suitable lead time suitable lead time and a timely and and a timely and effective effective disseminationdissemination

Heavy rainsnowmelt or high inflow

Data collection and transmission

Boundary estimation (rainfall tide )

Forecast calculation

Warning dissemination

Emergency action

Flooding starts

time

Meteorological forecast

Min

imis

em

axim

ise

Multiobjective Combinatorial Optimisation Multiobjective Combinatorial Optimisation Problems (MCOPs)Problems (MCOPs)

Many real-life applications involves two types of problem difficulty 1) multiple conflicting objectives 2) a highly complex search domain

Xnx2x1xxtosubjectxnfx2fx1fxfoptimizeOPCM

X is the search domain fi(x) is the ith objective function to be optimised

x is a set of decision vectors

Minimize cost

Question How do I combine all the fieldwork components and find the optimal schedule for receivers Therefore we need to answer not only the question

ldquo What is the best rdquo but also ldquoWhat is sufficiently robustrdquoAnswer Metaheuristic Techniques

MaximisationMaximisation(Global and local maxima)(Global and local maxima) Objective function

Iteration

Global maximum

Local maximum

In mechanical engineering an engineer wishes to design a car consisting of composite materials The engineer by optimization (maximization in this case) will conceivably design a lighter stronger attractive and safer composite car

In GPS surveying a surveyor wants to design a network taking in consideration components of the field-work The GPS surveyor by optimisation (minimization in this case) will design a cheaper and more acceptable schedule

Objective function

Iteration

Global minimum

Local minimum

MinimizationMinimization(Global and local minima)(Global and local minima)

Schematic Representation of the Search Schematic Representation of the Search Progress of Metaheuristic TechniquesProgress of Metaheuristic Techniques

S0

S2

S3

S4

S5

S6

S7

S8

S9

S10

S11

S12I(S0)

I(S4) I(S9)

I(S6)I(S12)

Q(S)

S1

Iterations

Values of Si

or the objective function

Select a given solution S I(S) and compute its value C(S) Generate a schedule Srsquo I(S) and compute its value C(Srsquo)If C(Srsquo) lt C(S) then Srsquo replaces S as a current solutionOtherwise retain S and generate other moves until C(Srsquo)ltC(S) for all Srsquo I(S)Terminate the search and return S as the local optimal solution

Q(S)I(S0)

S0

Metaheuristic TechniquesMetaheuristic Techniques bull Metaheuristic technique is an iterative self-learning procedure for

quickly and efficiently identifying a high quality solution for COPs

bull Types of Metaheuristics 1- Solution improvement techniques Simulated Annealing SA Tabu Search

TS 2- Solution construction techniques Ant Colony Optimisation ACO Genetic

Algorithm GA Neural Network NNbull Fundamental concepts of the

metaheuristics 1- Selection or construction of the initial

solution 2- Generation of the neighbouring solutions 3- Acceptance of solutions 4- Stopping criteria

Initial Solution Formation

Neighbourhood

Search by Move Formation(Local Search)

Provisional Neighbourhood Formation

Search by Solution Formation(Development and guided search)

Neighbourhood for the nextsolution formation

Acceptance Criteria

Termination of the Search

The Simulated Annealing (SA) TechniqueThe Simulated Annealing (SA) Technique

Crystalline solid (frozen) state (T=0) Amorphous solid (frozen) state (T=0)

Ucirc High free energy (high temperature)

The material as a system of particles(Cooling process)

Annealing (slow cooling) Quenching (fast cooling)

Ucirc Ucirc Global minimum of the energy Local minimum of the energy

Case II Case III

Case I

bull Based on analogy between combinatorial optimization and annealing process of solids

bull Improvement of solution for move from S to Srsquo always accepted

bull Accepts uphill move only with given probability (decreases in a number of rounds to zero)

bull Cooling parameters the initial starting value of the temperature the temperature length (Markov Chain) L the cooling ratio F

Temperature

Iterations

Stop

StartK

F

LTi

Tf

The Tabu Search (TS) techniqueThe Tabu Search (TS) technique

bull Add memory to LS (Prevent Cycling increase diversity of exploration encourage escape from local optima)

bull Avoids cycling (visiting same solution more than once) by use of short term memory for the very recent history and long-term memory for the distant history

bull TS parameters Size of Memry Contents of memory (solution based attributes eg changes to solution features) long term memory

bull Tabu Listbull Candidate Listbull Tabu Tenure

The modified ACO will have some major differences with a real (natural) one

bull Artificial ants will have some memorybull Artificial ants will not be completely blindbull Artificial ants will live in an environment

where time is discrete

The Ant Colony Optimization The Ant Colony Optimization ACOACO

ACOACO

We will finish on time Do not worry OK

bull Construction the Initial Schedule

bull Local Searchbull Local Updating Rule

bull Global Updating Rule

otherwise0

(r)Jsifrηrτsrηsrτ

s)(rPk

(r)Ju

β

β

kk

uu

srΔτρsrτρ1srτ

otherwise0

srifLs)(rΔτ

srΔταsrτα1srτ1

gb tourbestglobalwhere

The Genetic Algorithms (GAs)The Genetic Algorithms (GAs) The evolution process for generating a populationThe evolution process for generating a population

Father

Mother

Mutation

SelectionMating

Child 1

Next Generation

Crossover

Initial Generation

N

Chromosome

Gene

One-flip mutation in child 2

Child 2

One- point crossover

Neural Networks NNNeural Networks NN imitate the human brain imitate the human brain

NN consists of a collection of interconnected simple computational units that work together cooperatively (like synapses in the human brain) These units are input units for receiving information from the problem domain hidden units as internal weight structure and output units for broadcasting data

Different types of the GPS Surveying Different types of the GPS Surveying NetworksNetworks

The Seychelles GPS Network (linear) The Malta GPS Network (triangulation)

Handicapped Person Transportation in the City of Handicapped Person Transportation in the City of Bruxelles using Grouping Genetic AlgorithmBruxelles using Grouping Genetic Algorithm

D1

D2

(n)

O2

O1

(m)

D

O

Depot1

Depot2

The covered area by Depot2The covered area by Depot 1

0

2

3 1

4

LocationDirection of vehicleContour of networkRegion of massif influx

[Ostarti =1000 Oendi =1015] [Dstarti =1100 Dendi =1130]

Desired departure interval=15 mins Desired arrival interval=30 mins

Travel time=0060 mins [1010 -1110]

1110 1010

Oi (home) Di (hospital)

Disaster Warning Network for Danube Disaster Warning Network for Danube Basin (DWN)Basin (DWN)

bull DWN is a system of satellites and ground stations for providing real time early warning of the impact of a disaster

bull DWN implements a network of Control Stations (CSs) spread over the whole geographic area of the hazard and transmits their observations to a master station

bull DWN provides this reliable information on a continuous basis through the parallel process of coverage accuracy prediction and integrity risk simulation functions

Disaster Warning Network Disaster Warning Network for the Danube Basinfor the Danube Basin

Reference Receiver RR

Roving Receiver 2

Roving Receiver 3

Roving Receiver 1

SatelliteGPS Signal 1

Error correction message 1

Roving Receiver 4

GPS Signal 4

GPS Signal RR

GPS Signal 2

GPS Signal 3

Error correction message 4

Error correction message 2

Error correction message 3

Differential GPSDifferential GPS

The DWN model for Environmental The DWN model for Environmental Pollution ControlPollution Control

bull Multiobjective Optimisation Problems (MOPs)

bull f(x) is the objective function to be optimised and X is the search domain

bull Ps is a multiplier for measuring the size of the user population near the location s (eg safety teams)bull f(acy ing) are the evaluations of accuracy coverage and integrity performance respectively for user location

sbull f(cost) is the cost function of a given DWN designbull N is the number of user locationsbull n is the number of obtained DWN designs

Xxxxxtosubject

xfxfxfxfoptimizeMOP

n

n

21

21

CostfffPnF n

N

s

sing

sacysDWN

1

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

bull Dynamic Metahuristics Algorithms (DMA) potentially have these capabilities to produce set of high quality real-time designs that can model more closely and easily the objective functions and tolerate the noisy evaluations given by these functions

bull The final design should be robust (ie performs well over a wide range of environment conditions) and flexible (ie allows easy adaptation after the environment has changed)

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

bull DMAs can successfully handle a mix of continuous and discrete parameters as well as selecting individual components from database Therefore another objective of this project is to connect DWN to a database which includes all the related information (physical geographicaland biochemical) colleted by other observation techniques (eg Geographic Information System (GIS) Remote Sensing (RS) photogrametery internet air (water and soil) survey technology etc)

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

DWN can effectively optimise this problem by 1) providing access to a wide range of data types in real-time 2) combining the observational data with innovative data analysis to improve forecasting and risk assessment 3) and being a user friendly tool in decision support of environmental management

Current Applications (Earthquake) (1)Current Applications (Earthquake) (1)

bull GEO-INFORMATION TECHNOLOGY AND ARTIFICIAL INTELLIGENCE FOR OPTIMIZING THE DISPLACEMENTS OF EARTHQUAKES A CASE STUDY FOR SYRIAN ACTIVE FAULT ZONES

bull With Dr Mohamad Rukieh Syrian Ministry of Commuincation and Technology

bull International Union of Geodesy and Geophysics IUGG07bull Perugia Italy July 3-14 2007bull httpwwwiugg2007perugiait

Geographic Information ProcessGeographic Information Process Database amp VisualizationDatabase amp Visualization

The geographic information process consists of three stages Data acquisition data processing and data dissemination

Database ldquoNot easy to interpretrdquo

Visualisation ldquoWorth a Thousand wordsrdquo

Rules of Spatial Planning Rules of Spatial Planning

in Flood Managementin Flood Management flood reduction is part of sustainable development and the various action possibilities

to improve preventive flood protection based on spatial planning and urban development can be described as follow

bull Protection of existing retention areas eg declaration of flood areas etcbull Extension of retention areas eg creating detention ponds etcbull Retention in the catchments eg restoration of small streams etcbull Minimisation of damage potential eg preventive land-use management etcbull Technical flood protection measures eg dikes retention ponds etc

As shown from above flood risk can only effectively be reduced if in addition to the technical measures spatial planning regulates land-use in flood-prone areas

Flood Forecasting amp Early Warning

Provide accurate Provide accurate forecasts with a forecasts with a suitable lead time suitable lead time and a timely and and a timely and effective effective disseminationdissemination

Heavy rainsnowmelt or high inflow

Data collection and transmission

Boundary estimation (rainfall tide )

Forecast calculation

Warning dissemination

Emergency action

Flooding starts

time

Meteorological forecast

Min

imis

em

axim

ise

Multiobjective Combinatorial Optimisation Multiobjective Combinatorial Optimisation Problems (MCOPs)Problems (MCOPs)

Many real-life applications involves two types of problem difficulty 1) multiple conflicting objectives 2) a highly complex search domain

Xnx2x1xxtosubjectxnfx2fx1fxfoptimizeOPCM

X is the search domain fi(x) is the ith objective function to be optimised

x is a set of decision vectors

Minimize cost

Question How do I combine all the fieldwork components and find the optimal schedule for receivers Therefore we need to answer not only the question

ldquo What is the best rdquo but also ldquoWhat is sufficiently robustrdquoAnswer Metaheuristic Techniques

MaximisationMaximisation(Global and local maxima)(Global and local maxima) Objective function

Iteration

Global maximum

Local maximum

In mechanical engineering an engineer wishes to design a car consisting of composite materials The engineer by optimization (maximization in this case) will conceivably design a lighter stronger attractive and safer composite car

In GPS surveying a surveyor wants to design a network taking in consideration components of the field-work The GPS surveyor by optimisation (minimization in this case) will design a cheaper and more acceptable schedule

Objective function

Iteration

Global minimum

Local minimum

MinimizationMinimization(Global and local minima)(Global and local minima)

Schematic Representation of the Search Schematic Representation of the Search Progress of Metaheuristic TechniquesProgress of Metaheuristic Techniques

S0

S2

S3

S4

S5

S6

S7

S8

S9

S10

S11

S12I(S0)

I(S4) I(S9)

I(S6)I(S12)

Q(S)

S1

Iterations

Values of Si

or the objective function

Select a given solution S I(S) and compute its value C(S) Generate a schedule Srsquo I(S) and compute its value C(Srsquo)If C(Srsquo) lt C(S) then Srsquo replaces S as a current solutionOtherwise retain S and generate other moves until C(Srsquo)ltC(S) for all Srsquo I(S)Terminate the search and return S as the local optimal solution

Q(S)I(S0)

S0

Metaheuristic TechniquesMetaheuristic Techniques bull Metaheuristic technique is an iterative self-learning procedure for

quickly and efficiently identifying a high quality solution for COPs

bull Types of Metaheuristics 1- Solution improvement techniques Simulated Annealing SA Tabu Search

TS 2- Solution construction techniques Ant Colony Optimisation ACO Genetic

Algorithm GA Neural Network NNbull Fundamental concepts of the

metaheuristics 1- Selection or construction of the initial

solution 2- Generation of the neighbouring solutions 3- Acceptance of solutions 4- Stopping criteria

Initial Solution Formation

Neighbourhood

Search by Move Formation(Local Search)

Provisional Neighbourhood Formation

Search by Solution Formation(Development and guided search)

Neighbourhood for the nextsolution formation

Acceptance Criteria

Termination of the Search

The Simulated Annealing (SA) TechniqueThe Simulated Annealing (SA) Technique

Crystalline solid (frozen) state (T=0) Amorphous solid (frozen) state (T=0)

Ucirc High free energy (high temperature)

The material as a system of particles(Cooling process)

Annealing (slow cooling) Quenching (fast cooling)

Ucirc Ucirc Global minimum of the energy Local minimum of the energy

Case II Case III

Case I

bull Based on analogy between combinatorial optimization and annealing process of solids

bull Improvement of solution for move from S to Srsquo always accepted

bull Accepts uphill move only with given probability (decreases in a number of rounds to zero)

bull Cooling parameters the initial starting value of the temperature the temperature length (Markov Chain) L the cooling ratio F

Temperature

Iterations

Stop

StartK

F

LTi

Tf

The Tabu Search (TS) techniqueThe Tabu Search (TS) technique

bull Add memory to LS (Prevent Cycling increase diversity of exploration encourage escape from local optima)

bull Avoids cycling (visiting same solution more than once) by use of short term memory for the very recent history and long-term memory for the distant history

bull TS parameters Size of Memry Contents of memory (solution based attributes eg changes to solution features) long term memory

bull Tabu Listbull Candidate Listbull Tabu Tenure

The modified ACO will have some major differences with a real (natural) one

bull Artificial ants will have some memorybull Artificial ants will not be completely blindbull Artificial ants will live in an environment

where time is discrete

The Ant Colony Optimization The Ant Colony Optimization ACOACO

ACOACO

We will finish on time Do not worry OK

bull Construction the Initial Schedule

bull Local Searchbull Local Updating Rule

bull Global Updating Rule

otherwise0

(r)Jsifrηrτsrηsrτ

s)(rPk

(r)Ju

β

β

kk

uu

srΔτρsrτρ1srτ

otherwise0

srifLs)(rΔτ

srΔταsrτα1srτ1

gb tourbestglobalwhere

The Genetic Algorithms (GAs)The Genetic Algorithms (GAs) The evolution process for generating a populationThe evolution process for generating a population

Father

Mother

Mutation

SelectionMating

Child 1

Next Generation

Crossover

Initial Generation

N

Chromosome

Gene

One-flip mutation in child 2

Child 2

One- point crossover

Neural Networks NNNeural Networks NN imitate the human brain imitate the human brain

NN consists of a collection of interconnected simple computational units that work together cooperatively (like synapses in the human brain) These units are input units for receiving information from the problem domain hidden units as internal weight structure and output units for broadcasting data

Different types of the GPS Surveying Different types of the GPS Surveying NetworksNetworks

The Seychelles GPS Network (linear) The Malta GPS Network (triangulation)

Handicapped Person Transportation in the City of Handicapped Person Transportation in the City of Bruxelles using Grouping Genetic AlgorithmBruxelles using Grouping Genetic Algorithm

D1

D2

(n)

O2

O1

(m)

D

O

Depot1

Depot2

The covered area by Depot2The covered area by Depot 1

0

2

3 1

4

LocationDirection of vehicleContour of networkRegion of massif influx

[Ostarti =1000 Oendi =1015] [Dstarti =1100 Dendi =1130]

Desired departure interval=15 mins Desired arrival interval=30 mins

Travel time=0060 mins [1010 -1110]

1110 1010

Oi (home) Di (hospital)

Disaster Warning Network for Danube Disaster Warning Network for Danube Basin (DWN)Basin (DWN)

bull DWN is a system of satellites and ground stations for providing real time early warning of the impact of a disaster

bull DWN implements a network of Control Stations (CSs) spread over the whole geographic area of the hazard and transmits their observations to a master station

bull DWN provides this reliable information on a continuous basis through the parallel process of coverage accuracy prediction and integrity risk simulation functions

Disaster Warning Network Disaster Warning Network for the Danube Basinfor the Danube Basin

Reference Receiver RR

Roving Receiver 2

Roving Receiver 3

Roving Receiver 1

SatelliteGPS Signal 1

Error correction message 1

Roving Receiver 4

GPS Signal 4

GPS Signal RR

GPS Signal 2

GPS Signal 3

Error correction message 4

Error correction message 2

Error correction message 3

Differential GPSDifferential GPS

The DWN model for Environmental The DWN model for Environmental Pollution ControlPollution Control

bull Multiobjective Optimisation Problems (MOPs)

bull f(x) is the objective function to be optimised and X is the search domain

bull Ps is a multiplier for measuring the size of the user population near the location s (eg safety teams)bull f(acy ing) are the evaluations of accuracy coverage and integrity performance respectively for user location

sbull f(cost) is the cost function of a given DWN designbull N is the number of user locationsbull n is the number of obtained DWN designs

Xxxxxtosubject

xfxfxfxfoptimizeMOP

n

n

21

21

CostfffPnF n

N

s

sing

sacysDWN

1

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

bull Dynamic Metahuristics Algorithms (DMA) potentially have these capabilities to produce set of high quality real-time designs that can model more closely and easily the objective functions and tolerate the noisy evaluations given by these functions

bull The final design should be robust (ie performs well over a wide range of environment conditions) and flexible (ie allows easy adaptation after the environment has changed)

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

bull DMAs can successfully handle a mix of continuous and discrete parameters as well as selecting individual components from database Therefore another objective of this project is to connect DWN to a database which includes all the related information (physical geographicaland biochemical) colleted by other observation techniques (eg Geographic Information System (GIS) Remote Sensing (RS) photogrametery internet air (water and soil) survey technology etc)

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

DWN can effectively optimise this problem by 1) providing access to a wide range of data types in real-time 2) combining the observational data with innovative data analysis to improve forecasting and risk assessment 3) and being a user friendly tool in decision support of environmental management

Current Applications (Earthquake) (1)Current Applications (Earthquake) (1)

bull GEO-INFORMATION TECHNOLOGY AND ARTIFICIAL INTELLIGENCE FOR OPTIMIZING THE DISPLACEMENTS OF EARTHQUAKES A CASE STUDY FOR SYRIAN ACTIVE FAULT ZONES

bull With Dr Mohamad Rukieh Syrian Ministry of Commuincation and Technology

bull International Union of Geodesy and Geophysics IUGG07bull Perugia Italy July 3-14 2007bull httpwwwiugg2007perugiait

Rules of Spatial Planning Rules of Spatial Planning

in Flood Managementin Flood Management flood reduction is part of sustainable development and the various action possibilities

to improve preventive flood protection based on spatial planning and urban development can be described as follow

bull Protection of existing retention areas eg declaration of flood areas etcbull Extension of retention areas eg creating detention ponds etcbull Retention in the catchments eg restoration of small streams etcbull Minimisation of damage potential eg preventive land-use management etcbull Technical flood protection measures eg dikes retention ponds etc

As shown from above flood risk can only effectively be reduced if in addition to the technical measures spatial planning regulates land-use in flood-prone areas

Flood Forecasting amp Early Warning

Provide accurate Provide accurate forecasts with a forecasts with a suitable lead time suitable lead time and a timely and and a timely and effective effective disseminationdissemination

Heavy rainsnowmelt or high inflow

Data collection and transmission

Boundary estimation (rainfall tide )

Forecast calculation

Warning dissemination

Emergency action

Flooding starts

time

Meteorological forecast

Min

imis

em

axim

ise

Multiobjective Combinatorial Optimisation Multiobjective Combinatorial Optimisation Problems (MCOPs)Problems (MCOPs)

Many real-life applications involves two types of problem difficulty 1) multiple conflicting objectives 2) a highly complex search domain

Xnx2x1xxtosubjectxnfx2fx1fxfoptimizeOPCM

X is the search domain fi(x) is the ith objective function to be optimised

x is a set of decision vectors

Minimize cost

Question How do I combine all the fieldwork components and find the optimal schedule for receivers Therefore we need to answer not only the question

ldquo What is the best rdquo but also ldquoWhat is sufficiently robustrdquoAnswer Metaheuristic Techniques

MaximisationMaximisation(Global and local maxima)(Global and local maxima) Objective function

Iteration

Global maximum

Local maximum

In mechanical engineering an engineer wishes to design a car consisting of composite materials The engineer by optimization (maximization in this case) will conceivably design a lighter stronger attractive and safer composite car

In GPS surveying a surveyor wants to design a network taking in consideration components of the field-work The GPS surveyor by optimisation (minimization in this case) will design a cheaper and more acceptable schedule

Objective function

Iteration

Global minimum

Local minimum

MinimizationMinimization(Global and local minima)(Global and local minima)

Schematic Representation of the Search Schematic Representation of the Search Progress of Metaheuristic TechniquesProgress of Metaheuristic Techniques

S0

S2

S3

S4

S5

S6

S7

S8

S9

S10

S11

S12I(S0)

I(S4) I(S9)

I(S6)I(S12)

Q(S)

S1

Iterations

Values of Si

or the objective function

Select a given solution S I(S) and compute its value C(S) Generate a schedule Srsquo I(S) and compute its value C(Srsquo)If C(Srsquo) lt C(S) then Srsquo replaces S as a current solutionOtherwise retain S and generate other moves until C(Srsquo)ltC(S) for all Srsquo I(S)Terminate the search and return S as the local optimal solution

Q(S)I(S0)

S0

Metaheuristic TechniquesMetaheuristic Techniques bull Metaheuristic technique is an iterative self-learning procedure for

quickly and efficiently identifying a high quality solution for COPs

bull Types of Metaheuristics 1- Solution improvement techniques Simulated Annealing SA Tabu Search

TS 2- Solution construction techniques Ant Colony Optimisation ACO Genetic

Algorithm GA Neural Network NNbull Fundamental concepts of the

metaheuristics 1- Selection or construction of the initial

solution 2- Generation of the neighbouring solutions 3- Acceptance of solutions 4- Stopping criteria

Initial Solution Formation

Neighbourhood

Search by Move Formation(Local Search)

Provisional Neighbourhood Formation

Search by Solution Formation(Development and guided search)

Neighbourhood for the nextsolution formation

Acceptance Criteria

Termination of the Search

The Simulated Annealing (SA) TechniqueThe Simulated Annealing (SA) Technique

Crystalline solid (frozen) state (T=0) Amorphous solid (frozen) state (T=0)

Ucirc High free energy (high temperature)

The material as a system of particles(Cooling process)

Annealing (slow cooling) Quenching (fast cooling)

Ucirc Ucirc Global minimum of the energy Local minimum of the energy

Case II Case III

Case I

bull Based on analogy between combinatorial optimization and annealing process of solids

bull Improvement of solution for move from S to Srsquo always accepted

bull Accepts uphill move only with given probability (decreases in a number of rounds to zero)

bull Cooling parameters the initial starting value of the temperature the temperature length (Markov Chain) L the cooling ratio F

Temperature

Iterations

Stop

StartK

F

LTi

Tf

The Tabu Search (TS) techniqueThe Tabu Search (TS) technique

bull Add memory to LS (Prevent Cycling increase diversity of exploration encourage escape from local optima)

bull Avoids cycling (visiting same solution more than once) by use of short term memory for the very recent history and long-term memory for the distant history

bull TS parameters Size of Memry Contents of memory (solution based attributes eg changes to solution features) long term memory

bull Tabu Listbull Candidate Listbull Tabu Tenure

The modified ACO will have some major differences with a real (natural) one

bull Artificial ants will have some memorybull Artificial ants will not be completely blindbull Artificial ants will live in an environment

where time is discrete

The Ant Colony Optimization The Ant Colony Optimization ACOACO

ACOACO

We will finish on time Do not worry OK

bull Construction the Initial Schedule

bull Local Searchbull Local Updating Rule

bull Global Updating Rule

otherwise0

(r)Jsifrηrτsrηsrτ

s)(rPk

(r)Ju

β

β

kk

uu

srΔτρsrτρ1srτ

otherwise0

srifLs)(rΔτ

srΔταsrτα1srτ1

gb tourbestglobalwhere

The Genetic Algorithms (GAs)The Genetic Algorithms (GAs) The evolution process for generating a populationThe evolution process for generating a population

Father

Mother

Mutation

SelectionMating

Child 1

Next Generation

Crossover

Initial Generation

N

Chromosome

Gene

One-flip mutation in child 2

Child 2

One- point crossover

Neural Networks NNNeural Networks NN imitate the human brain imitate the human brain

NN consists of a collection of interconnected simple computational units that work together cooperatively (like synapses in the human brain) These units are input units for receiving information from the problem domain hidden units as internal weight structure and output units for broadcasting data

Different types of the GPS Surveying Different types of the GPS Surveying NetworksNetworks

The Seychelles GPS Network (linear) The Malta GPS Network (triangulation)

Handicapped Person Transportation in the City of Handicapped Person Transportation in the City of Bruxelles using Grouping Genetic AlgorithmBruxelles using Grouping Genetic Algorithm

D1

D2

(n)

O2

O1

(m)

D

O

Depot1

Depot2

The covered area by Depot2The covered area by Depot 1

0

2

3 1

4

LocationDirection of vehicleContour of networkRegion of massif influx

[Ostarti =1000 Oendi =1015] [Dstarti =1100 Dendi =1130]

Desired departure interval=15 mins Desired arrival interval=30 mins

Travel time=0060 mins [1010 -1110]

1110 1010

Oi (home) Di (hospital)

Disaster Warning Network for Danube Disaster Warning Network for Danube Basin (DWN)Basin (DWN)

bull DWN is a system of satellites and ground stations for providing real time early warning of the impact of a disaster

bull DWN implements a network of Control Stations (CSs) spread over the whole geographic area of the hazard and transmits their observations to a master station

bull DWN provides this reliable information on a continuous basis through the parallel process of coverage accuracy prediction and integrity risk simulation functions

Disaster Warning Network Disaster Warning Network for the Danube Basinfor the Danube Basin

Reference Receiver RR

Roving Receiver 2

Roving Receiver 3

Roving Receiver 1

SatelliteGPS Signal 1

Error correction message 1

Roving Receiver 4

GPS Signal 4

GPS Signal RR

GPS Signal 2

GPS Signal 3

Error correction message 4

Error correction message 2

Error correction message 3

Differential GPSDifferential GPS

The DWN model for Environmental The DWN model for Environmental Pollution ControlPollution Control

bull Multiobjective Optimisation Problems (MOPs)

bull f(x) is the objective function to be optimised and X is the search domain

bull Ps is a multiplier for measuring the size of the user population near the location s (eg safety teams)bull f(acy ing) are the evaluations of accuracy coverage and integrity performance respectively for user location

sbull f(cost) is the cost function of a given DWN designbull N is the number of user locationsbull n is the number of obtained DWN designs

Xxxxxtosubject

xfxfxfxfoptimizeMOP

n

n

21

21

CostfffPnF n

N

s

sing

sacysDWN

1

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

bull Dynamic Metahuristics Algorithms (DMA) potentially have these capabilities to produce set of high quality real-time designs that can model more closely and easily the objective functions and tolerate the noisy evaluations given by these functions

bull The final design should be robust (ie performs well over a wide range of environment conditions) and flexible (ie allows easy adaptation after the environment has changed)

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

bull DMAs can successfully handle a mix of continuous and discrete parameters as well as selecting individual components from database Therefore another objective of this project is to connect DWN to a database which includes all the related information (physical geographicaland biochemical) colleted by other observation techniques (eg Geographic Information System (GIS) Remote Sensing (RS) photogrametery internet air (water and soil) survey technology etc)

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

DWN can effectively optimise this problem by 1) providing access to a wide range of data types in real-time 2) combining the observational data with innovative data analysis to improve forecasting and risk assessment 3) and being a user friendly tool in decision support of environmental management

Current Applications (Earthquake) (1)Current Applications (Earthquake) (1)

bull GEO-INFORMATION TECHNOLOGY AND ARTIFICIAL INTELLIGENCE FOR OPTIMIZING THE DISPLACEMENTS OF EARTHQUAKES A CASE STUDY FOR SYRIAN ACTIVE FAULT ZONES

bull With Dr Mohamad Rukieh Syrian Ministry of Commuincation and Technology

bull International Union of Geodesy and Geophysics IUGG07bull Perugia Italy July 3-14 2007bull httpwwwiugg2007perugiait

Flood Forecasting amp Early Warning

Provide accurate Provide accurate forecasts with a forecasts with a suitable lead time suitable lead time and a timely and and a timely and effective effective disseminationdissemination

Heavy rainsnowmelt or high inflow

Data collection and transmission

Boundary estimation (rainfall tide )

Forecast calculation

Warning dissemination

Emergency action

Flooding starts

time

Meteorological forecast

Min

imis

em

axim

ise

Multiobjective Combinatorial Optimisation Multiobjective Combinatorial Optimisation Problems (MCOPs)Problems (MCOPs)

Many real-life applications involves two types of problem difficulty 1) multiple conflicting objectives 2) a highly complex search domain

Xnx2x1xxtosubjectxnfx2fx1fxfoptimizeOPCM

X is the search domain fi(x) is the ith objective function to be optimised

x is a set of decision vectors

Minimize cost

Question How do I combine all the fieldwork components and find the optimal schedule for receivers Therefore we need to answer not only the question

ldquo What is the best rdquo but also ldquoWhat is sufficiently robustrdquoAnswer Metaheuristic Techniques

MaximisationMaximisation(Global and local maxima)(Global and local maxima) Objective function

Iteration

Global maximum

Local maximum

In mechanical engineering an engineer wishes to design a car consisting of composite materials The engineer by optimization (maximization in this case) will conceivably design a lighter stronger attractive and safer composite car

In GPS surveying a surveyor wants to design a network taking in consideration components of the field-work The GPS surveyor by optimisation (minimization in this case) will design a cheaper and more acceptable schedule

Objective function

Iteration

Global minimum

Local minimum

MinimizationMinimization(Global and local minima)(Global and local minima)

Schematic Representation of the Search Schematic Representation of the Search Progress of Metaheuristic TechniquesProgress of Metaheuristic Techniques

S0

S2

S3

S4

S5

S6

S7

S8

S9

S10

S11

S12I(S0)

I(S4) I(S9)

I(S6)I(S12)

Q(S)

S1

Iterations

Values of Si

or the objective function

Select a given solution S I(S) and compute its value C(S) Generate a schedule Srsquo I(S) and compute its value C(Srsquo)If C(Srsquo) lt C(S) then Srsquo replaces S as a current solutionOtherwise retain S and generate other moves until C(Srsquo)ltC(S) for all Srsquo I(S)Terminate the search and return S as the local optimal solution

Q(S)I(S0)

S0

Metaheuristic TechniquesMetaheuristic Techniques bull Metaheuristic technique is an iterative self-learning procedure for

quickly and efficiently identifying a high quality solution for COPs

bull Types of Metaheuristics 1- Solution improvement techniques Simulated Annealing SA Tabu Search

TS 2- Solution construction techniques Ant Colony Optimisation ACO Genetic

Algorithm GA Neural Network NNbull Fundamental concepts of the

metaheuristics 1- Selection or construction of the initial

solution 2- Generation of the neighbouring solutions 3- Acceptance of solutions 4- Stopping criteria

Initial Solution Formation

Neighbourhood

Search by Move Formation(Local Search)

Provisional Neighbourhood Formation

Search by Solution Formation(Development and guided search)

Neighbourhood for the nextsolution formation

Acceptance Criteria

Termination of the Search

The Simulated Annealing (SA) TechniqueThe Simulated Annealing (SA) Technique

Crystalline solid (frozen) state (T=0) Amorphous solid (frozen) state (T=0)

Ucirc High free energy (high temperature)

The material as a system of particles(Cooling process)

Annealing (slow cooling) Quenching (fast cooling)

Ucirc Ucirc Global minimum of the energy Local minimum of the energy

Case II Case III

Case I

bull Based on analogy between combinatorial optimization and annealing process of solids

bull Improvement of solution for move from S to Srsquo always accepted

bull Accepts uphill move only with given probability (decreases in a number of rounds to zero)

bull Cooling parameters the initial starting value of the temperature the temperature length (Markov Chain) L the cooling ratio F

Temperature

Iterations

Stop

StartK

F

LTi

Tf

The Tabu Search (TS) techniqueThe Tabu Search (TS) technique

bull Add memory to LS (Prevent Cycling increase diversity of exploration encourage escape from local optima)

bull Avoids cycling (visiting same solution more than once) by use of short term memory for the very recent history and long-term memory for the distant history

bull TS parameters Size of Memry Contents of memory (solution based attributes eg changes to solution features) long term memory

bull Tabu Listbull Candidate Listbull Tabu Tenure

The modified ACO will have some major differences with a real (natural) one

bull Artificial ants will have some memorybull Artificial ants will not be completely blindbull Artificial ants will live in an environment

where time is discrete

The Ant Colony Optimization The Ant Colony Optimization ACOACO

ACOACO

We will finish on time Do not worry OK

bull Construction the Initial Schedule

bull Local Searchbull Local Updating Rule

bull Global Updating Rule

otherwise0

(r)Jsifrηrτsrηsrτ

s)(rPk

(r)Ju

β

β

kk

uu

srΔτρsrτρ1srτ

otherwise0

srifLs)(rΔτ

srΔταsrτα1srτ1

gb tourbestglobalwhere

The Genetic Algorithms (GAs)The Genetic Algorithms (GAs) The evolution process for generating a populationThe evolution process for generating a population

Father

Mother

Mutation

SelectionMating

Child 1

Next Generation

Crossover

Initial Generation

N

Chromosome

Gene

One-flip mutation in child 2

Child 2

One- point crossover

Neural Networks NNNeural Networks NN imitate the human brain imitate the human brain

NN consists of a collection of interconnected simple computational units that work together cooperatively (like synapses in the human brain) These units are input units for receiving information from the problem domain hidden units as internal weight structure and output units for broadcasting data

Different types of the GPS Surveying Different types of the GPS Surveying NetworksNetworks

The Seychelles GPS Network (linear) The Malta GPS Network (triangulation)

Handicapped Person Transportation in the City of Handicapped Person Transportation in the City of Bruxelles using Grouping Genetic AlgorithmBruxelles using Grouping Genetic Algorithm

D1

D2

(n)

O2

O1

(m)

D

O

Depot1

Depot2

The covered area by Depot2The covered area by Depot 1

0

2

3 1

4

LocationDirection of vehicleContour of networkRegion of massif influx

[Ostarti =1000 Oendi =1015] [Dstarti =1100 Dendi =1130]

Desired departure interval=15 mins Desired arrival interval=30 mins

Travel time=0060 mins [1010 -1110]

1110 1010

Oi (home) Di (hospital)

Disaster Warning Network for Danube Disaster Warning Network for Danube Basin (DWN)Basin (DWN)

bull DWN is a system of satellites and ground stations for providing real time early warning of the impact of a disaster

bull DWN implements a network of Control Stations (CSs) spread over the whole geographic area of the hazard and transmits their observations to a master station

bull DWN provides this reliable information on a continuous basis through the parallel process of coverage accuracy prediction and integrity risk simulation functions

Disaster Warning Network Disaster Warning Network for the Danube Basinfor the Danube Basin

Reference Receiver RR

Roving Receiver 2

Roving Receiver 3

Roving Receiver 1

SatelliteGPS Signal 1

Error correction message 1

Roving Receiver 4

GPS Signal 4

GPS Signal RR

GPS Signal 2

GPS Signal 3

Error correction message 4

Error correction message 2

Error correction message 3

Differential GPSDifferential GPS

The DWN model for Environmental The DWN model for Environmental Pollution ControlPollution Control

bull Multiobjective Optimisation Problems (MOPs)

bull f(x) is the objective function to be optimised and X is the search domain

bull Ps is a multiplier for measuring the size of the user population near the location s (eg safety teams)bull f(acy ing) are the evaluations of accuracy coverage and integrity performance respectively for user location

sbull f(cost) is the cost function of a given DWN designbull N is the number of user locationsbull n is the number of obtained DWN designs

Xxxxxtosubject

xfxfxfxfoptimizeMOP

n

n

21

21

CostfffPnF n

N

s

sing

sacysDWN

1

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

bull Dynamic Metahuristics Algorithms (DMA) potentially have these capabilities to produce set of high quality real-time designs that can model more closely and easily the objective functions and tolerate the noisy evaluations given by these functions

bull The final design should be robust (ie performs well over a wide range of environment conditions) and flexible (ie allows easy adaptation after the environment has changed)

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

bull DMAs can successfully handle a mix of continuous and discrete parameters as well as selecting individual components from database Therefore another objective of this project is to connect DWN to a database which includes all the related information (physical geographicaland biochemical) colleted by other observation techniques (eg Geographic Information System (GIS) Remote Sensing (RS) photogrametery internet air (water and soil) survey technology etc)

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

DWN can effectively optimise this problem by 1) providing access to a wide range of data types in real-time 2) combining the observational data with innovative data analysis to improve forecasting and risk assessment 3) and being a user friendly tool in decision support of environmental management

Current Applications (Earthquake) (1)Current Applications (Earthquake) (1)

bull GEO-INFORMATION TECHNOLOGY AND ARTIFICIAL INTELLIGENCE FOR OPTIMIZING THE DISPLACEMENTS OF EARTHQUAKES A CASE STUDY FOR SYRIAN ACTIVE FAULT ZONES

bull With Dr Mohamad Rukieh Syrian Ministry of Commuincation and Technology

bull International Union of Geodesy and Geophysics IUGG07bull Perugia Italy July 3-14 2007bull httpwwwiugg2007perugiait

Multiobjective Combinatorial Optimisation Multiobjective Combinatorial Optimisation Problems (MCOPs)Problems (MCOPs)

Many real-life applications involves two types of problem difficulty 1) multiple conflicting objectives 2) a highly complex search domain

Xnx2x1xxtosubjectxnfx2fx1fxfoptimizeOPCM

X is the search domain fi(x) is the ith objective function to be optimised

x is a set of decision vectors

Minimize cost

Question How do I combine all the fieldwork components and find the optimal schedule for receivers Therefore we need to answer not only the question

ldquo What is the best rdquo but also ldquoWhat is sufficiently robustrdquoAnswer Metaheuristic Techniques

MaximisationMaximisation(Global and local maxima)(Global and local maxima) Objective function

Iteration

Global maximum

Local maximum

In mechanical engineering an engineer wishes to design a car consisting of composite materials The engineer by optimization (maximization in this case) will conceivably design a lighter stronger attractive and safer composite car

In GPS surveying a surveyor wants to design a network taking in consideration components of the field-work The GPS surveyor by optimisation (minimization in this case) will design a cheaper and more acceptable schedule

Objective function

Iteration

Global minimum

Local minimum

MinimizationMinimization(Global and local minima)(Global and local minima)

Schematic Representation of the Search Schematic Representation of the Search Progress of Metaheuristic TechniquesProgress of Metaheuristic Techniques

S0

S2

S3

S4

S5

S6

S7

S8

S9

S10

S11

S12I(S0)

I(S4) I(S9)

I(S6)I(S12)

Q(S)

S1

Iterations

Values of Si

or the objective function

Select a given solution S I(S) and compute its value C(S) Generate a schedule Srsquo I(S) and compute its value C(Srsquo)If C(Srsquo) lt C(S) then Srsquo replaces S as a current solutionOtherwise retain S and generate other moves until C(Srsquo)ltC(S) for all Srsquo I(S)Terminate the search and return S as the local optimal solution

Q(S)I(S0)

S0

Metaheuristic TechniquesMetaheuristic Techniques bull Metaheuristic technique is an iterative self-learning procedure for

quickly and efficiently identifying a high quality solution for COPs

bull Types of Metaheuristics 1- Solution improvement techniques Simulated Annealing SA Tabu Search

TS 2- Solution construction techniques Ant Colony Optimisation ACO Genetic

Algorithm GA Neural Network NNbull Fundamental concepts of the

metaheuristics 1- Selection or construction of the initial

solution 2- Generation of the neighbouring solutions 3- Acceptance of solutions 4- Stopping criteria

Initial Solution Formation

Neighbourhood

Search by Move Formation(Local Search)

Provisional Neighbourhood Formation

Search by Solution Formation(Development and guided search)

Neighbourhood for the nextsolution formation

Acceptance Criteria

Termination of the Search

The Simulated Annealing (SA) TechniqueThe Simulated Annealing (SA) Technique

Crystalline solid (frozen) state (T=0) Amorphous solid (frozen) state (T=0)

Ucirc High free energy (high temperature)

The material as a system of particles(Cooling process)

Annealing (slow cooling) Quenching (fast cooling)

Ucirc Ucirc Global minimum of the energy Local minimum of the energy

Case II Case III

Case I

bull Based on analogy between combinatorial optimization and annealing process of solids

bull Improvement of solution for move from S to Srsquo always accepted

bull Accepts uphill move only with given probability (decreases in a number of rounds to zero)

bull Cooling parameters the initial starting value of the temperature the temperature length (Markov Chain) L the cooling ratio F

Temperature

Iterations

Stop

StartK

F

LTi

Tf

The Tabu Search (TS) techniqueThe Tabu Search (TS) technique

bull Add memory to LS (Prevent Cycling increase diversity of exploration encourage escape from local optima)

bull Avoids cycling (visiting same solution more than once) by use of short term memory for the very recent history and long-term memory for the distant history

bull TS parameters Size of Memry Contents of memory (solution based attributes eg changes to solution features) long term memory

bull Tabu Listbull Candidate Listbull Tabu Tenure

The modified ACO will have some major differences with a real (natural) one

bull Artificial ants will have some memorybull Artificial ants will not be completely blindbull Artificial ants will live in an environment

where time is discrete

The Ant Colony Optimization The Ant Colony Optimization ACOACO

ACOACO

We will finish on time Do not worry OK

bull Construction the Initial Schedule

bull Local Searchbull Local Updating Rule

bull Global Updating Rule

otherwise0

(r)Jsifrηrτsrηsrτ

s)(rPk

(r)Ju

β

β

kk

uu

srΔτρsrτρ1srτ

otherwise0

srifLs)(rΔτ

srΔταsrτα1srτ1

gb tourbestglobalwhere

The Genetic Algorithms (GAs)The Genetic Algorithms (GAs) The evolution process for generating a populationThe evolution process for generating a population

Father

Mother

Mutation

SelectionMating

Child 1

Next Generation

Crossover

Initial Generation

N

Chromosome

Gene

One-flip mutation in child 2

Child 2

One- point crossover

Neural Networks NNNeural Networks NN imitate the human brain imitate the human brain

NN consists of a collection of interconnected simple computational units that work together cooperatively (like synapses in the human brain) These units are input units for receiving information from the problem domain hidden units as internal weight structure and output units for broadcasting data

Different types of the GPS Surveying Different types of the GPS Surveying NetworksNetworks

The Seychelles GPS Network (linear) The Malta GPS Network (triangulation)

Handicapped Person Transportation in the City of Handicapped Person Transportation in the City of Bruxelles using Grouping Genetic AlgorithmBruxelles using Grouping Genetic Algorithm

D1

D2

(n)

O2

O1

(m)

D

O

Depot1

Depot2

The covered area by Depot2The covered area by Depot 1

0

2

3 1

4

LocationDirection of vehicleContour of networkRegion of massif influx

[Ostarti =1000 Oendi =1015] [Dstarti =1100 Dendi =1130]

Desired departure interval=15 mins Desired arrival interval=30 mins

Travel time=0060 mins [1010 -1110]

1110 1010

Oi (home) Di (hospital)

Disaster Warning Network for Danube Disaster Warning Network for Danube Basin (DWN)Basin (DWN)

bull DWN is a system of satellites and ground stations for providing real time early warning of the impact of a disaster

bull DWN implements a network of Control Stations (CSs) spread over the whole geographic area of the hazard and transmits their observations to a master station

bull DWN provides this reliable information on a continuous basis through the parallel process of coverage accuracy prediction and integrity risk simulation functions

Disaster Warning Network Disaster Warning Network for the Danube Basinfor the Danube Basin

Reference Receiver RR

Roving Receiver 2

Roving Receiver 3

Roving Receiver 1

SatelliteGPS Signal 1

Error correction message 1

Roving Receiver 4

GPS Signal 4

GPS Signal RR

GPS Signal 2

GPS Signal 3

Error correction message 4

Error correction message 2

Error correction message 3

Differential GPSDifferential GPS

The DWN model for Environmental The DWN model for Environmental Pollution ControlPollution Control

bull Multiobjective Optimisation Problems (MOPs)

bull f(x) is the objective function to be optimised and X is the search domain

bull Ps is a multiplier for measuring the size of the user population near the location s (eg safety teams)bull f(acy ing) are the evaluations of accuracy coverage and integrity performance respectively for user location

sbull f(cost) is the cost function of a given DWN designbull N is the number of user locationsbull n is the number of obtained DWN designs

Xxxxxtosubject

xfxfxfxfoptimizeMOP

n

n

21

21

CostfffPnF n

N

s

sing

sacysDWN

1

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

bull Dynamic Metahuristics Algorithms (DMA) potentially have these capabilities to produce set of high quality real-time designs that can model more closely and easily the objective functions and tolerate the noisy evaluations given by these functions

bull The final design should be robust (ie performs well over a wide range of environment conditions) and flexible (ie allows easy adaptation after the environment has changed)

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

bull DMAs can successfully handle a mix of continuous and discrete parameters as well as selecting individual components from database Therefore another objective of this project is to connect DWN to a database which includes all the related information (physical geographicaland biochemical) colleted by other observation techniques (eg Geographic Information System (GIS) Remote Sensing (RS) photogrametery internet air (water and soil) survey technology etc)

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

DWN can effectively optimise this problem by 1) providing access to a wide range of data types in real-time 2) combining the observational data with innovative data analysis to improve forecasting and risk assessment 3) and being a user friendly tool in decision support of environmental management

Current Applications (Earthquake) (1)Current Applications (Earthquake) (1)

bull GEO-INFORMATION TECHNOLOGY AND ARTIFICIAL INTELLIGENCE FOR OPTIMIZING THE DISPLACEMENTS OF EARTHQUAKES A CASE STUDY FOR SYRIAN ACTIVE FAULT ZONES

bull With Dr Mohamad Rukieh Syrian Ministry of Commuincation and Technology

bull International Union of Geodesy and Geophysics IUGG07bull Perugia Italy July 3-14 2007bull httpwwwiugg2007perugiait

MaximisationMaximisation(Global and local maxima)(Global and local maxima) Objective function

Iteration

Global maximum

Local maximum

In mechanical engineering an engineer wishes to design a car consisting of composite materials The engineer by optimization (maximization in this case) will conceivably design a lighter stronger attractive and safer composite car

In GPS surveying a surveyor wants to design a network taking in consideration components of the field-work The GPS surveyor by optimisation (minimization in this case) will design a cheaper and more acceptable schedule

Objective function

Iteration

Global minimum

Local minimum

MinimizationMinimization(Global and local minima)(Global and local minima)

Schematic Representation of the Search Schematic Representation of the Search Progress of Metaheuristic TechniquesProgress of Metaheuristic Techniques

S0

S2

S3

S4

S5

S6

S7

S8

S9

S10

S11

S12I(S0)

I(S4) I(S9)

I(S6)I(S12)

Q(S)

S1

Iterations

Values of Si

or the objective function

Select a given solution S I(S) and compute its value C(S) Generate a schedule Srsquo I(S) and compute its value C(Srsquo)If C(Srsquo) lt C(S) then Srsquo replaces S as a current solutionOtherwise retain S and generate other moves until C(Srsquo)ltC(S) for all Srsquo I(S)Terminate the search and return S as the local optimal solution

Q(S)I(S0)

S0

Metaheuristic TechniquesMetaheuristic Techniques bull Metaheuristic technique is an iterative self-learning procedure for

quickly and efficiently identifying a high quality solution for COPs

bull Types of Metaheuristics 1- Solution improvement techniques Simulated Annealing SA Tabu Search

TS 2- Solution construction techniques Ant Colony Optimisation ACO Genetic

Algorithm GA Neural Network NNbull Fundamental concepts of the

metaheuristics 1- Selection or construction of the initial

solution 2- Generation of the neighbouring solutions 3- Acceptance of solutions 4- Stopping criteria

Initial Solution Formation

Neighbourhood

Search by Move Formation(Local Search)

Provisional Neighbourhood Formation

Search by Solution Formation(Development and guided search)

Neighbourhood for the nextsolution formation

Acceptance Criteria

Termination of the Search

The Simulated Annealing (SA) TechniqueThe Simulated Annealing (SA) Technique

Crystalline solid (frozen) state (T=0) Amorphous solid (frozen) state (T=0)

Ucirc High free energy (high temperature)

The material as a system of particles(Cooling process)

Annealing (slow cooling) Quenching (fast cooling)

Ucirc Ucirc Global minimum of the energy Local minimum of the energy

Case II Case III

Case I

bull Based on analogy between combinatorial optimization and annealing process of solids

bull Improvement of solution for move from S to Srsquo always accepted

bull Accepts uphill move only with given probability (decreases in a number of rounds to zero)

bull Cooling parameters the initial starting value of the temperature the temperature length (Markov Chain) L the cooling ratio F

Temperature

Iterations

Stop

StartK

F

LTi

Tf

The Tabu Search (TS) techniqueThe Tabu Search (TS) technique

bull Add memory to LS (Prevent Cycling increase diversity of exploration encourage escape from local optima)

bull Avoids cycling (visiting same solution more than once) by use of short term memory for the very recent history and long-term memory for the distant history

bull TS parameters Size of Memry Contents of memory (solution based attributes eg changes to solution features) long term memory

bull Tabu Listbull Candidate Listbull Tabu Tenure

The modified ACO will have some major differences with a real (natural) one

bull Artificial ants will have some memorybull Artificial ants will not be completely blindbull Artificial ants will live in an environment

where time is discrete

The Ant Colony Optimization The Ant Colony Optimization ACOACO

ACOACO

We will finish on time Do not worry OK

bull Construction the Initial Schedule

bull Local Searchbull Local Updating Rule

bull Global Updating Rule

otherwise0

(r)Jsifrηrτsrηsrτ

s)(rPk

(r)Ju

β

β

kk

uu

srΔτρsrτρ1srτ

otherwise0

srifLs)(rΔτ

srΔταsrτα1srτ1

gb tourbestglobalwhere

The Genetic Algorithms (GAs)The Genetic Algorithms (GAs) The evolution process for generating a populationThe evolution process for generating a population

Father

Mother

Mutation

SelectionMating

Child 1

Next Generation

Crossover

Initial Generation

N

Chromosome

Gene

One-flip mutation in child 2

Child 2

One- point crossover

Neural Networks NNNeural Networks NN imitate the human brain imitate the human brain

NN consists of a collection of interconnected simple computational units that work together cooperatively (like synapses in the human brain) These units are input units for receiving information from the problem domain hidden units as internal weight structure and output units for broadcasting data

Different types of the GPS Surveying Different types of the GPS Surveying NetworksNetworks

The Seychelles GPS Network (linear) The Malta GPS Network (triangulation)

Handicapped Person Transportation in the City of Handicapped Person Transportation in the City of Bruxelles using Grouping Genetic AlgorithmBruxelles using Grouping Genetic Algorithm

D1

D2

(n)

O2

O1

(m)

D

O

Depot1

Depot2

The covered area by Depot2The covered area by Depot 1

0

2

3 1

4

LocationDirection of vehicleContour of networkRegion of massif influx

[Ostarti =1000 Oendi =1015] [Dstarti =1100 Dendi =1130]

Desired departure interval=15 mins Desired arrival interval=30 mins

Travel time=0060 mins [1010 -1110]

1110 1010

Oi (home) Di (hospital)

Disaster Warning Network for Danube Disaster Warning Network for Danube Basin (DWN)Basin (DWN)

bull DWN is a system of satellites and ground stations for providing real time early warning of the impact of a disaster

bull DWN implements a network of Control Stations (CSs) spread over the whole geographic area of the hazard and transmits their observations to a master station

bull DWN provides this reliable information on a continuous basis through the parallel process of coverage accuracy prediction and integrity risk simulation functions

Disaster Warning Network Disaster Warning Network for the Danube Basinfor the Danube Basin

Reference Receiver RR

Roving Receiver 2

Roving Receiver 3

Roving Receiver 1

SatelliteGPS Signal 1

Error correction message 1

Roving Receiver 4

GPS Signal 4

GPS Signal RR

GPS Signal 2

GPS Signal 3

Error correction message 4

Error correction message 2

Error correction message 3

Differential GPSDifferential GPS

The DWN model for Environmental The DWN model for Environmental Pollution ControlPollution Control

bull Multiobjective Optimisation Problems (MOPs)

bull f(x) is the objective function to be optimised and X is the search domain

bull Ps is a multiplier for measuring the size of the user population near the location s (eg safety teams)bull f(acy ing) are the evaluations of accuracy coverage and integrity performance respectively for user location

sbull f(cost) is the cost function of a given DWN designbull N is the number of user locationsbull n is the number of obtained DWN designs

Xxxxxtosubject

xfxfxfxfoptimizeMOP

n

n

21

21

CostfffPnF n

N

s

sing

sacysDWN

1

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

bull Dynamic Metahuristics Algorithms (DMA) potentially have these capabilities to produce set of high quality real-time designs that can model more closely and easily the objective functions and tolerate the noisy evaluations given by these functions

bull The final design should be robust (ie performs well over a wide range of environment conditions) and flexible (ie allows easy adaptation after the environment has changed)

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

bull DMAs can successfully handle a mix of continuous and discrete parameters as well as selecting individual components from database Therefore another objective of this project is to connect DWN to a database which includes all the related information (physical geographicaland biochemical) colleted by other observation techniques (eg Geographic Information System (GIS) Remote Sensing (RS) photogrametery internet air (water and soil) survey technology etc)

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

DWN can effectively optimise this problem by 1) providing access to a wide range of data types in real-time 2) combining the observational data with innovative data analysis to improve forecasting and risk assessment 3) and being a user friendly tool in decision support of environmental management

Current Applications (Earthquake) (1)Current Applications (Earthquake) (1)

bull GEO-INFORMATION TECHNOLOGY AND ARTIFICIAL INTELLIGENCE FOR OPTIMIZING THE DISPLACEMENTS OF EARTHQUAKES A CASE STUDY FOR SYRIAN ACTIVE FAULT ZONES

bull With Dr Mohamad Rukieh Syrian Ministry of Commuincation and Technology

bull International Union of Geodesy and Geophysics IUGG07bull Perugia Italy July 3-14 2007bull httpwwwiugg2007perugiait

Schematic Representation of the Search Schematic Representation of the Search Progress of Metaheuristic TechniquesProgress of Metaheuristic Techniques

S0

S2

S3

S4

S5

S6

S7

S8

S9

S10

S11

S12I(S0)

I(S4) I(S9)

I(S6)I(S12)

Q(S)

S1

Iterations

Values of Si

or the objective function

Select a given solution S I(S) and compute its value C(S) Generate a schedule Srsquo I(S) and compute its value C(Srsquo)If C(Srsquo) lt C(S) then Srsquo replaces S as a current solutionOtherwise retain S and generate other moves until C(Srsquo)ltC(S) for all Srsquo I(S)Terminate the search and return S as the local optimal solution

Q(S)I(S0)

S0

Metaheuristic TechniquesMetaheuristic Techniques bull Metaheuristic technique is an iterative self-learning procedure for

quickly and efficiently identifying a high quality solution for COPs

bull Types of Metaheuristics 1- Solution improvement techniques Simulated Annealing SA Tabu Search

TS 2- Solution construction techniques Ant Colony Optimisation ACO Genetic

Algorithm GA Neural Network NNbull Fundamental concepts of the

metaheuristics 1- Selection or construction of the initial

solution 2- Generation of the neighbouring solutions 3- Acceptance of solutions 4- Stopping criteria

Initial Solution Formation

Neighbourhood

Search by Move Formation(Local Search)

Provisional Neighbourhood Formation

Search by Solution Formation(Development and guided search)

Neighbourhood for the nextsolution formation

Acceptance Criteria

Termination of the Search

The Simulated Annealing (SA) TechniqueThe Simulated Annealing (SA) Technique

Crystalline solid (frozen) state (T=0) Amorphous solid (frozen) state (T=0)

Ucirc High free energy (high temperature)

The material as a system of particles(Cooling process)

Annealing (slow cooling) Quenching (fast cooling)

Ucirc Ucirc Global minimum of the energy Local minimum of the energy

Case II Case III

Case I

bull Based on analogy between combinatorial optimization and annealing process of solids

bull Improvement of solution for move from S to Srsquo always accepted

bull Accepts uphill move only with given probability (decreases in a number of rounds to zero)

bull Cooling parameters the initial starting value of the temperature the temperature length (Markov Chain) L the cooling ratio F

Temperature

Iterations

Stop

StartK

F

LTi

Tf

The Tabu Search (TS) techniqueThe Tabu Search (TS) technique

bull Add memory to LS (Prevent Cycling increase diversity of exploration encourage escape from local optima)

bull Avoids cycling (visiting same solution more than once) by use of short term memory for the very recent history and long-term memory for the distant history

bull TS parameters Size of Memry Contents of memory (solution based attributes eg changes to solution features) long term memory

bull Tabu Listbull Candidate Listbull Tabu Tenure

The modified ACO will have some major differences with a real (natural) one

bull Artificial ants will have some memorybull Artificial ants will not be completely blindbull Artificial ants will live in an environment

where time is discrete

The Ant Colony Optimization The Ant Colony Optimization ACOACO

ACOACO

We will finish on time Do not worry OK

bull Construction the Initial Schedule

bull Local Searchbull Local Updating Rule

bull Global Updating Rule

otherwise0

(r)Jsifrηrτsrηsrτ

s)(rPk

(r)Ju

β

β

kk

uu

srΔτρsrτρ1srτ

otherwise0

srifLs)(rΔτ

srΔταsrτα1srτ1

gb tourbestglobalwhere

The Genetic Algorithms (GAs)The Genetic Algorithms (GAs) The evolution process for generating a populationThe evolution process for generating a population

Father

Mother

Mutation

SelectionMating

Child 1

Next Generation

Crossover

Initial Generation

N

Chromosome

Gene

One-flip mutation in child 2

Child 2

One- point crossover

Neural Networks NNNeural Networks NN imitate the human brain imitate the human brain

NN consists of a collection of interconnected simple computational units that work together cooperatively (like synapses in the human brain) These units are input units for receiving information from the problem domain hidden units as internal weight structure and output units for broadcasting data

Different types of the GPS Surveying Different types of the GPS Surveying NetworksNetworks

The Seychelles GPS Network (linear) The Malta GPS Network (triangulation)

Handicapped Person Transportation in the City of Handicapped Person Transportation in the City of Bruxelles using Grouping Genetic AlgorithmBruxelles using Grouping Genetic Algorithm

D1

D2

(n)

O2

O1

(m)

D

O

Depot1

Depot2

The covered area by Depot2The covered area by Depot 1

0

2

3 1

4

LocationDirection of vehicleContour of networkRegion of massif influx

[Ostarti =1000 Oendi =1015] [Dstarti =1100 Dendi =1130]

Desired departure interval=15 mins Desired arrival interval=30 mins

Travel time=0060 mins [1010 -1110]

1110 1010

Oi (home) Di (hospital)

Disaster Warning Network for Danube Disaster Warning Network for Danube Basin (DWN)Basin (DWN)

bull DWN is a system of satellites and ground stations for providing real time early warning of the impact of a disaster

bull DWN implements a network of Control Stations (CSs) spread over the whole geographic area of the hazard and transmits their observations to a master station

bull DWN provides this reliable information on a continuous basis through the parallel process of coverage accuracy prediction and integrity risk simulation functions

Disaster Warning Network Disaster Warning Network for the Danube Basinfor the Danube Basin

Reference Receiver RR

Roving Receiver 2

Roving Receiver 3

Roving Receiver 1

SatelliteGPS Signal 1

Error correction message 1

Roving Receiver 4

GPS Signal 4

GPS Signal RR

GPS Signal 2

GPS Signal 3

Error correction message 4

Error correction message 2

Error correction message 3

Differential GPSDifferential GPS

The DWN model for Environmental The DWN model for Environmental Pollution ControlPollution Control

bull Multiobjective Optimisation Problems (MOPs)

bull f(x) is the objective function to be optimised and X is the search domain

bull Ps is a multiplier for measuring the size of the user population near the location s (eg safety teams)bull f(acy ing) are the evaluations of accuracy coverage and integrity performance respectively for user location

sbull f(cost) is the cost function of a given DWN designbull N is the number of user locationsbull n is the number of obtained DWN designs

Xxxxxtosubject

xfxfxfxfoptimizeMOP

n

n

21

21

CostfffPnF n

N

s

sing

sacysDWN

1

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

bull Dynamic Metahuristics Algorithms (DMA) potentially have these capabilities to produce set of high quality real-time designs that can model more closely and easily the objective functions and tolerate the noisy evaluations given by these functions

bull The final design should be robust (ie performs well over a wide range of environment conditions) and flexible (ie allows easy adaptation after the environment has changed)

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

bull DMAs can successfully handle a mix of continuous and discrete parameters as well as selecting individual components from database Therefore another objective of this project is to connect DWN to a database which includes all the related information (physical geographicaland biochemical) colleted by other observation techniques (eg Geographic Information System (GIS) Remote Sensing (RS) photogrametery internet air (water and soil) survey technology etc)

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

DWN can effectively optimise this problem by 1) providing access to a wide range of data types in real-time 2) combining the observational data with innovative data analysis to improve forecasting and risk assessment 3) and being a user friendly tool in decision support of environmental management

Current Applications (Earthquake) (1)Current Applications (Earthquake) (1)

bull GEO-INFORMATION TECHNOLOGY AND ARTIFICIAL INTELLIGENCE FOR OPTIMIZING THE DISPLACEMENTS OF EARTHQUAKES A CASE STUDY FOR SYRIAN ACTIVE FAULT ZONES

bull With Dr Mohamad Rukieh Syrian Ministry of Commuincation and Technology

bull International Union of Geodesy and Geophysics IUGG07bull Perugia Italy July 3-14 2007bull httpwwwiugg2007perugiait

Metaheuristic TechniquesMetaheuristic Techniques bull Metaheuristic technique is an iterative self-learning procedure for

quickly and efficiently identifying a high quality solution for COPs

bull Types of Metaheuristics 1- Solution improvement techniques Simulated Annealing SA Tabu Search

TS 2- Solution construction techniques Ant Colony Optimisation ACO Genetic

Algorithm GA Neural Network NNbull Fundamental concepts of the

metaheuristics 1- Selection or construction of the initial

solution 2- Generation of the neighbouring solutions 3- Acceptance of solutions 4- Stopping criteria

Initial Solution Formation

Neighbourhood

Search by Move Formation(Local Search)

Provisional Neighbourhood Formation

Search by Solution Formation(Development and guided search)

Neighbourhood for the nextsolution formation

Acceptance Criteria

Termination of the Search

The Simulated Annealing (SA) TechniqueThe Simulated Annealing (SA) Technique

Crystalline solid (frozen) state (T=0) Amorphous solid (frozen) state (T=0)

Ucirc High free energy (high temperature)

The material as a system of particles(Cooling process)

Annealing (slow cooling) Quenching (fast cooling)

Ucirc Ucirc Global minimum of the energy Local minimum of the energy

Case II Case III

Case I

bull Based on analogy between combinatorial optimization and annealing process of solids

bull Improvement of solution for move from S to Srsquo always accepted

bull Accepts uphill move only with given probability (decreases in a number of rounds to zero)

bull Cooling parameters the initial starting value of the temperature the temperature length (Markov Chain) L the cooling ratio F

Temperature

Iterations

Stop

StartK

F

LTi

Tf

The Tabu Search (TS) techniqueThe Tabu Search (TS) technique

bull Add memory to LS (Prevent Cycling increase diversity of exploration encourage escape from local optima)

bull Avoids cycling (visiting same solution more than once) by use of short term memory for the very recent history and long-term memory for the distant history

bull TS parameters Size of Memry Contents of memory (solution based attributes eg changes to solution features) long term memory

bull Tabu Listbull Candidate Listbull Tabu Tenure

The modified ACO will have some major differences with a real (natural) one

bull Artificial ants will have some memorybull Artificial ants will not be completely blindbull Artificial ants will live in an environment

where time is discrete

The Ant Colony Optimization The Ant Colony Optimization ACOACO

ACOACO

We will finish on time Do not worry OK

bull Construction the Initial Schedule

bull Local Searchbull Local Updating Rule

bull Global Updating Rule

otherwise0

(r)Jsifrηrτsrηsrτ

s)(rPk

(r)Ju

β

β

kk

uu

srΔτρsrτρ1srτ

otherwise0

srifLs)(rΔτ

srΔταsrτα1srτ1

gb tourbestglobalwhere

The Genetic Algorithms (GAs)The Genetic Algorithms (GAs) The evolution process for generating a populationThe evolution process for generating a population

Father

Mother

Mutation

SelectionMating

Child 1

Next Generation

Crossover

Initial Generation

N

Chromosome

Gene

One-flip mutation in child 2

Child 2

One- point crossover

Neural Networks NNNeural Networks NN imitate the human brain imitate the human brain

NN consists of a collection of interconnected simple computational units that work together cooperatively (like synapses in the human brain) These units are input units for receiving information from the problem domain hidden units as internal weight structure and output units for broadcasting data

Different types of the GPS Surveying Different types of the GPS Surveying NetworksNetworks

The Seychelles GPS Network (linear) The Malta GPS Network (triangulation)

Handicapped Person Transportation in the City of Handicapped Person Transportation in the City of Bruxelles using Grouping Genetic AlgorithmBruxelles using Grouping Genetic Algorithm

D1

D2

(n)

O2

O1

(m)

D

O

Depot1

Depot2

The covered area by Depot2The covered area by Depot 1

0

2

3 1

4

LocationDirection of vehicleContour of networkRegion of massif influx

[Ostarti =1000 Oendi =1015] [Dstarti =1100 Dendi =1130]

Desired departure interval=15 mins Desired arrival interval=30 mins

Travel time=0060 mins [1010 -1110]

1110 1010

Oi (home) Di (hospital)

Disaster Warning Network for Danube Disaster Warning Network for Danube Basin (DWN)Basin (DWN)

bull DWN is a system of satellites and ground stations for providing real time early warning of the impact of a disaster

bull DWN implements a network of Control Stations (CSs) spread over the whole geographic area of the hazard and transmits their observations to a master station

bull DWN provides this reliable information on a continuous basis through the parallel process of coverage accuracy prediction and integrity risk simulation functions

Disaster Warning Network Disaster Warning Network for the Danube Basinfor the Danube Basin

Reference Receiver RR

Roving Receiver 2

Roving Receiver 3

Roving Receiver 1

SatelliteGPS Signal 1

Error correction message 1

Roving Receiver 4

GPS Signal 4

GPS Signal RR

GPS Signal 2

GPS Signal 3

Error correction message 4

Error correction message 2

Error correction message 3

Differential GPSDifferential GPS

The DWN model for Environmental The DWN model for Environmental Pollution ControlPollution Control

bull Multiobjective Optimisation Problems (MOPs)

bull f(x) is the objective function to be optimised and X is the search domain

bull Ps is a multiplier for measuring the size of the user population near the location s (eg safety teams)bull f(acy ing) are the evaluations of accuracy coverage and integrity performance respectively for user location

sbull f(cost) is the cost function of a given DWN designbull N is the number of user locationsbull n is the number of obtained DWN designs

Xxxxxtosubject

xfxfxfxfoptimizeMOP

n

n

21

21

CostfffPnF n

N

s

sing

sacysDWN

1

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

bull Dynamic Metahuristics Algorithms (DMA) potentially have these capabilities to produce set of high quality real-time designs that can model more closely and easily the objective functions and tolerate the noisy evaluations given by these functions

bull The final design should be robust (ie performs well over a wide range of environment conditions) and flexible (ie allows easy adaptation after the environment has changed)

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

bull DMAs can successfully handle a mix of continuous and discrete parameters as well as selecting individual components from database Therefore another objective of this project is to connect DWN to a database which includes all the related information (physical geographicaland biochemical) colleted by other observation techniques (eg Geographic Information System (GIS) Remote Sensing (RS) photogrametery internet air (water and soil) survey technology etc)

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

DWN can effectively optimise this problem by 1) providing access to a wide range of data types in real-time 2) combining the observational data with innovative data analysis to improve forecasting and risk assessment 3) and being a user friendly tool in decision support of environmental management

Current Applications (Earthquake) (1)Current Applications (Earthquake) (1)

bull GEO-INFORMATION TECHNOLOGY AND ARTIFICIAL INTELLIGENCE FOR OPTIMIZING THE DISPLACEMENTS OF EARTHQUAKES A CASE STUDY FOR SYRIAN ACTIVE FAULT ZONES

bull With Dr Mohamad Rukieh Syrian Ministry of Commuincation and Technology

bull International Union of Geodesy and Geophysics IUGG07bull Perugia Italy July 3-14 2007bull httpwwwiugg2007perugiait

The Simulated Annealing (SA) TechniqueThe Simulated Annealing (SA) Technique

Crystalline solid (frozen) state (T=0) Amorphous solid (frozen) state (T=0)

Ucirc High free energy (high temperature)

The material as a system of particles(Cooling process)

Annealing (slow cooling) Quenching (fast cooling)

Ucirc Ucirc Global minimum of the energy Local minimum of the energy

Case II Case III

Case I

bull Based on analogy between combinatorial optimization and annealing process of solids

bull Improvement of solution for move from S to Srsquo always accepted

bull Accepts uphill move only with given probability (decreases in a number of rounds to zero)

bull Cooling parameters the initial starting value of the temperature the temperature length (Markov Chain) L the cooling ratio F

Temperature

Iterations

Stop

StartK

F

LTi

Tf

The Tabu Search (TS) techniqueThe Tabu Search (TS) technique

bull Add memory to LS (Prevent Cycling increase diversity of exploration encourage escape from local optima)

bull Avoids cycling (visiting same solution more than once) by use of short term memory for the very recent history and long-term memory for the distant history

bull TS parameters Size of Memry Contents of memory (solution based attributes eg changes to solution features) long term memory

bull Tabu Listbull Candidate Listbull Tabu Tenure

The modified ACO will have some major differences with a real (natural) one

bull Artificial ants will have some memorybull Artificial ants will not be completely blindbull Artificial ants will live in an environment

where time is discrete

The Ant Colony Optimization The Ant Colony Optimization ACOACO

ACOACO

We will finish on time Do not worry OK

bull Construction the Initial Schedule

bull Local Searchbull Local Updating Rule

bull Global Updating Rule

otherwise0

(r)Jsifrηrτsrηsrτ

s)(rPk

(r)Ju

β

β

kk

uu

srΔτρsrτρ1srτ

otherwise0

srifLs)(rΔτ

srΔταsrτα1srτ1

gb tourbestglobalwhere

The Genetic Algorithms (GAs)The Genetic Algorithms (GAs) The evolution process for generating a populationThe evolution process for generating a population

Father

Mother

Mutation

SelectionMating

Child 1

Next Generation

Crossover

Initial Generation

N

Chromosome

Gene

One-flip mutation in child 2

Child 2

One- point crossover

Neural Networks NNNeural Networks NN imitate the human brain imitate the human brain

NN consists of a collection of interconnected simple computational units that work together cooperatively (like synapses in the human brain) These units are input units for receiving information from the problem domain hidden units as internal weight structure and output units for broadcasting data

Different types of the GPS Surveying Different types of the GPS Surveying NetworksNetworks

The Seychelles GPS Network (linear) The Malta GPS Network (triangulation)

Handicapped Person Transportation in the City of Handicapped Person Transportation in the City of Bruxelles using Grouping Genetic AlgorithmBruxelles using Grouping Genetic Algorithm

D1

D2

(n)

O2

O1

(m)

D

O

Depot1

Depot2

The covered area by Depot2The covered area by Depot 1

0

2

3 1

4

LocationDirection of vehicleContour of networkRegion of massif influx

[Ostarti =1000 Oendi =1015] [Dstarti =1100 Dendi =1130]

Desired departure interval=15 mins Desired arrival interval=30 mins

Travel time=0060 mins [1010 -1110]

1110 1010

Oi (home) Di (hospital)

Disaster Warning Network for Danube Disaster Warning Network for Danube Basin (DWN)Basin (DWN)

bull DWN is a system of satellites and ground stations for providing real time early warning of the impact of a disaster

bull DWN implements a network of Control Stations (CSs) spread over the whole geographic area of the hazard and transmits their observations to a master station

bull DWN provides this reliable information on a continuous basis through the parallel process of coverage accuracy prediction and integrity risk simulation functions

Disaster Warning Network Disaster Warning Network for the Danube Basinfor the Danube Basin

Reference Receiver RR

Roving Receiver 2

Roving Receiver 3

Roving Receiver 1

SatelliteGPS Signal 1

Error correction message 1

Roving Receiver 4

GPS Signal 4

GPS Signal RR

GPS Signal 2

GPS Signal 3

Error correction message 4

Error correction message 2

Error correction message 3

Differential GPSDifferential GPS

The DWN model for Environmental The DWN model for Environmental Pollution ControlPollution Control

bull Multiobjective Optimisation Problems (MOPs)

bull f(x) is the objective function to be optimised and X is the search domain

bull Ps is a multiplier for measuring the size of the user population near the location s (eg safety teams)bull f(acy ing) are the evaluations of accuracy coverage and integrity performance respectively for user location

sbull f(cost) is the cost function of a given DWN designbull N is the number of user locationsbull n is the number of obtained DWN designs

Xxxxxtosubject

xfxfxfxfoptimizeMOP

n

n

21

21

CostfffPnF n

N

s

sing

sacysDWN

1

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

bull Dynamic Metahuristics Algorithms (DMA) potentially have these capabilities to produce set of high quality real-time designs that can model more closely and easily the objective functions and tolerate the noisy evaluations given by these functions

bull The final design should be robust (ie performs well over a wide range of environment conditions) and flexible (ie allows easy adaptation after the environment has changed)

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

bull DMAs can successfully handle a mix of continuous and discrete parameters as well as selecting individual components from database Therefore another objective of this project is to connect DWN to a database which includes all the related information (physical geographicaland biochemical) colleted by other observation techniques (eg Geographic Information System (GIS) Remote Sensing (RS) photogrametery internet air (water and soil) survey technology etc)

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

DWN can effectively optimise this problem by 1) providing access to a wide range of data types in real-time 2) combining the observational data with innovative data analysis to improve forecasting and risk assessment 3) and being a user friendly tool in decision support of environmental management

Current Applications (Earthquake) (1)Current Applications (Earthquake) (1)

bull GEO-INFORMATION TECHNOLOGY AND ARTIFICIAL INTELLIGENCE FOR OPTIMIZING THE DISPLACEMENTS OF EARTHQUAKES A CASE STUDY FOR SYRIAN ACTIVE FAULT ZONES

bull With Dr Mohamad Rukieh Syrian Ministry of Commuincation and Technology

bull International Union of Geodesy and Geophysics IUGG07bull Perugia Italy July 3-14 2007bull httpwwwiugg2007perugiait

The Tabu Search (TS) techniqueThe Tabu Search (TS) technique

bull Add memory to LS (Prevent Cycling increase diversity of exploration encourage escape from local optima)

bull Avoids cycling (visiting same solution more than once) by use of short term memory for the very recent history and long-term memory for the distant history

bull TS parameters Size of Memry Contents of memory (solution based attributes eg changes to solution features) long term memory

bull Tabu Listbull Candidate Listbull Tabu Tenure

The modified ACO will have some major differences with a real (natural) one

bull Artificial ants will have some memorybull Artificial ants will not be completely blindbull Artificial ants will live in an environment

where time is discrete

The Ant Colony Optimization The Ant Colony Optimization ACOACO

ACOACO

We will finish on time Do not worry OK

bull Construction the Initial Schedule

bull Local Searchbull Local Updating Rule

bull Global Updating Rule

otherwise0

(r)Jsifrηrτsrηsrτ

s)(rPk

(r)Ju

β

β

kk

uu

srΔτρsrτρ1srτ

otherwise0

srifLs)(rΔτ

srΔταsrτα1srτ1

gb tourbestglobalwhere

The Genetic Algorithms (GAs)The Genetic Algorithms (GAs) The evolution process for generating a populationThe evolution process for generating a population

Father

Mother

Mutation

SelectionMating

Child 1

Next Generation

Crossover

Initial Generation

N

Chromosome

Gene

One-flip mutation in child 2

Child 2

One- point crossover

Neural Networks NNNeural Networks NN imitate the human brain imitate the human brain

NN consists of a collection of interconnected simple computational units that work together cooperatively (like synapses in the human brain) These units are input units for receiving information from the problem domain hidden units as internal weight structure and output units for broadcasting data

Different types of the GPS Surveying Different types of the GPS Surveying NetworksNetworks

The Seychelles GPS Network (linear) The Malta GPS Network (triangulation)

Handicapped Person Transportation in the City of Handicapped Person Transportation in the City of Bruxelles using Grouping Genetic AlgorithmBruxelles using Grouping Genetic Algorithm

D1

D2

(n)

O2

O1

(m)

D

O

Depot1

Depot2

The covered area by Depot2The covered area by Depot 1

0

2

3 1

4

LocationDirection of vehicleContour of networkRegion of massif influx

[Ostarti =1000 Oendi =1015] [Dstarti =1100 Dendi =1130]

Desired departure interval=15 mins Desired arrival interval=30 mins

Travel time=0060 mins [1010 -1110]

1110 1010

Oi (home) Di (hospital)

Disaster Warning Network for Danube Disaster Warning Network for Danube Basin (DWN)Basin (DWN)

bull DWN is a system of satellites and ground stations for providing real time early warning of the impact of a disaster

bull DWN implements a network of Control Stations (CSs) spread over the whole geographic area of the hazard and transmits their observations to a master station

bull DWN provides this reliable information on a continuous basis through the parallel process of coverage accuracy prediction and integrity risk simulation functions

Disaster Warning Network Disaster Warning Network for the Danube Basinfor the Danube Basin

Reference Receiver RR

Roving Receiver 2

Roving Receiver 3

Roving Receiver 1

SatelliteGPS Signal 1

Error correction message 1

Roving Receiver 4

GPS Signal 4

GPS Signal RR

GPS Signal 2

GPS Signal 3

Error correction message 4

Error correction message 2

Error correction message 3

Differential GPSDifferential GPS

The DWN model for Environmental The DWN model for Environmental Pollution ControlPollution Control

bull Multiobjective Optimisation Problems (MOPs)

bull f(x) is the objective function to be optimised and X is the search domain

bull Ps is a multiplier for measuring the size of the user population near the location s (eg safety teams)bull f(acy ing) are the evaluations of accuracy coverage and integrity performance respectively for user location

sbull f(cost) is the cost function of a given DWN designbull N is the number of user locationsbull n is the number of obtained DWN designs

Xxxxxtosubject

xfxfxfxfoptimizeMOP

n

n

21

21

CostfffPnF n

N

s

sing

sacysDWN

1

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

bull Dynamic Metahuristics Algorithms (DMA) potentially have these capabilities to produce set of high quality real-time designs that can model more closely and easily the objective functions and tolerate the noisy evaluations given by these functions

bull The final design should be robust (ie performs well over a wide range of environment conditions) and flexible (ie allows easy adaptation after the environment has changed)

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

bull DMAs can successfully handle a mix of continuous and discrete parameters as well as selecting individual components from database Therefore another objective of this project is to connect DWN to a database which includes all the related information (physical geographicaland biochemical) colleted by other observation techniques (eg Geographic Information System (GIS) Remote Sensing (RS) photogrametery internet air (water and soil) survey technology etc)

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

DWN can effectively optimise this problem by 1) providing access to a wide range of data types in real-time 2) combining the observational data with innovative data analysis to improve forecasting and risk assessment 3) and being a user friendly tool in decision support of environmental management

Current Applications (Earthquake) (1)Current Applications (Earthquake) (1)

bull GEO-INFORMATION TECHNOLOGY AND ARTIFICIAL INTELLIGENCE FOR OPTIMIZING THE DISPLACEMENTS OF EARTHQUAKES A CASE STUDY FOR SYRIAN ACTIVE FAULT ZONES

bull With Dr Mohamad Rukieh Syrian Ministry of Commuincation and Technology

bull International Union of Geodesy and Geophysics IUGG07bull Perugia Italy July 3-14 2007bull httpwwwiugg2007perugiait

The modified ACO will have some major differences with a real (natural) one

bull Artificial ants will have some memorybull Artificial ants will not be completely blindbull Artificial ants will live in an environment

where time is discrete

The Ant Colony Optimization The Ant Colony Optimization ACOACO

ACOACO

We will finish on time Do not worry OK

bull Construction the Initial Schedule

bull Local Searchbull Local Updating Rule

bull Global Updating Rule

otherwise0

(r)Jsifrηrτsrηsrτ

s)(rPk

(r)Ju

β

β

kk

uu

srΔτρsrτρ1srτ

otherwise0

srifLs)(rΔτ

srΔταsrτα1srτ1

gb tourbestglobalwhere

The Genetic Algorithms (GAs)The Genetic Algorithms (GAs) The evolution process for generating a populationThe evolution process for generating a population

Father

Mother

Mutation

SelectionMating

Child 1

Next Generation

Crossover

Initial Generation

N

Chromosome

Gene

One-flip mutation in child 2

Child 2

One- point crossover

Neural Networks NNNeural Networks NN imitate the human brain imitate the human brain

NN consists of a collection of interconnected simple computational units that work together cooperatively (like synapses in the human brain) These units are input units for receiving information from the problem domain hidden units as internal weight structure and output units for broadcasting data

Different types of the GPS Surveying Different types of the GPS Surveying NetworksNetworks

The Seychelles GPS Network (linear) The Malta GPS Network (triangulation)

Handicapped Person Transportation in the City of Handicapped Person Transportation in the City of Bruxelles using Grouping Genetic AlgorithmBruxelles using Grouping Genetic Algorithm

D1

D2

(n)

O2

O1

(m)

D

O

Depot1

Depot2

The covered area by Depot2The covered area by Depot 1

0

2

3 1

4

LocationDirection of vehicleContour of networkRegion of massif influx

[Ostarti =1000 Oendi =1015] [Dstarti =1100 Dendi =1130]

Desired departure interval=15 mins Desired arrival interval=30 mins

Travel time=0060 mins [1010 -1110]

1110 1010

Oi (home) Di (hospital)

Disaster Warning Network for Danube Disaster Warning Network for Danube Basin (DWN)Basin (DWN)

bull DWN is a system of satellites and ground stations for providing real time early warning of the impact of a disaster

bull DWN implements a network of Control Stations (CSs) spread over the whole geographic area of the hazard and transmits their observations to a master station

bull DWN provides this reliable information on a continuous basis through the parallel process of coverage accuracy prediction and integrity risk simulation functions

Disaster Warning Network Disaster Warning Network for the Danube Basinfor the Danube Basin

Reference Receiver RR

Roving Receiver 2

Roving Receiver 3

Roving Receiver 1

SatelliteGPS Signal 1

Error correction message 1

Roving Receiver 4

GPS Signal 4

GPS Signal RR

GPS Signal 2

GPS Signal 3

Error correction message 4

Error correction message 2

Error correction message 3

Differential GPSDifferential GPS

The DWN model for Environmental The DWN model for Environmental Pollution ControlPollution Control

bull Multiobjective Optimisation Problems (MOPs)

bull f(x) is the objective function to be optimised and X is the search domain

bull Ps is a multiplier for measuring the size of the user population near the location s (eg safety teams)bull f(acy ing) are the evaluations of accuracy coverage and integrity performance respectively for user location

sbull f(cost) is the cost function of a given DWN designbull N is the number of user locationsbull n is the number of obtained DWN designs

Xxxxxtosubject

xfxfxfxfoptimizeMOP

n

n

21

21

CostfffPnF n

N

s

sing

sacysDWN

1

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

bull Dynamic Metahuristics Algorithms (DMA) potentially have these capabilities to produce set of high quality real-time designs that can model more closely and easily the objective functions and tolerate the noisy evaluations given by these functions

bull The final design should be robust (ie performs well over a wide range of environment conditions) and flexible (ie allows easy adaptation after the environment has changed)

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

bull DMAs can successfully handle a mix of continuous and discrete parameters as well as selecting individual components from database Therefore another objective of this project is to connect DWN to a database which includes all the related information (physical geographicaland biochemical) colleted by other observation techniques (eg Geographic Information System (GIS) Remote Sensing (RS) photogrametery internet air (water and soil) survey technology etc)

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

DWN can effectively optimise this problem by 1) providing access to a wide range of data types in real-time 2) combining the observational data with innovative data analysis to improve forecasting and risk assessment 3) and being a user friendly tool in decision support of environmental management

Current Applications (Earthquake) (1)Current Applications (Earthquake) (1)

bull GEO-INFORMATION TECHNOLOGY AND ARTIFICIAL INTELLIGENCE FOR OPTIMIZING THE DISPLACEMENTS OF EARTHQUAKES A CASE STUDY FOR SYRIAN ACTIVE FAULT ZONES

bull With Dr Mohamad Rukieh Syrian Ministry of Commuincation and Technology

bull International Union of Geodesy and Geophysics IUGG07bull Perugia Italy July 3-14 2007bull httpwwwiugg2007perugiait

ACOACO

We will finish on time Do not worry OK

bull Construction the Initial Schedule

bull Local Searchbull Local Updating Rule

bull Global Updating Rule

otherwise0

(r)Jsifrηrτsrηsrτ

s)(rPk

(r)Ju

β

β

kk

uu

srΔτρsrτρ1srτ

otherwise0

srifLs)(rΔτ

srΔταsrτα1srτ1

gb tourbestglobalwhere

The Genetic Algorithms (GAs)The Genetic Algorithms (GAs) The evolution process for generating a populationThe evolution process for generating a population

Father

Mother

Mutation

SelectionMating

Child 1

Next Generation

Crossover

Initial Generation

N

Chromosome

Gene

One-flip mutation in child 2

Child 2

One- point crossover

Neural Networks NNNeural Networks NN imitate the human brain imitate the human brain

NN consists of a collection of interconnected simple computational units that work together cooperatively (like synapses in the human brain) These units are input units for receiving information from the problem domain hidden units as internal weight structure and output units for broadcasting data

Different types of the GPS Surveying Different types of the GPS Surveying NetworksNetworks

The Seychelles GPS Network (linear) The Malta GPS Network (triangulation)

Handicapped Person Transportation in the City of Handicapped Person Transportation in the City of Bruxelles using Grouping Genetic AlgorithmBruxelles using Grouping Genetic Algorithm

D1

D2

(n)

O2

O1

(m)

D

O

Depot1

Depot2

The covered area by Depot2The covered area by Depot 1

0

2

3 1

4

LocationDirection of vehicleContour of networkRegion of massif influx

[Ostarti =1000 Oendi =1015] [Dstarti =1100 Dendi =1130]

Desired departure interval=15 mins Desired arrival interval=30 mins

Travel time=0060 mins [1010 -1110]

1110 1010

Oi (home) Di (hospital)

Disaster Warning Network for Danube Disaster Warning Network for Danube Basin (DWN)Basin (DWN)

bull DWN is a system of satellites and ground stations for providing real time early warning of the impact of a disaster

bull DWN implements a network of Control Stations (CSs) spread over the whole geographic area of the hazard and transmits their observations to a master station

bull DWN provides this reliable information on a continuous basis through the parallel process of coverage accuracy prediction and integrity risk simulation functions

Disaster Warning Network Disaster Warning Network for the Danube Basinfor the Danube Basin

Reference Receiver RR

Roving Receiver 2

Roving Receiver 3

Roving Receiver 1

SatelliteGPS Signal 1

Error correction message 1

Roving Receiver 4

GPS Signal 4

GPS Signal RR

GPS Signal 2

GPS Signal 3

Error correction message 4

Error correction message 2

Error correction message 3

Differential GPSDifferential GPS

The DWN model for Environmental The DWN model for Environmental Pollution ControlPollution Control

bull Multiobjective Optimisation Problems (MOPs)

bull f(x) is the objective function to be optimised and X is the search domain

bull Ps is a multiplier for measuring the size of the user population near the location s (eg safety teams)bull f(acy ing) are the evaluations of accuracy coverage and integrity performance respectively for user location

sbull f(cost) is the cost function of a given DWN designbull N is the number of user locationsbull n is the number of obtained DWN designs

Xxxxxtosubject

xfxfxfxfoptimizeMOP

n

n

21

21

CostfffPnF n

N

s

sing

sacysDWN

1

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

bull Dynamic Metahuristics Algorithms (DMA) potentially have these capabilities to produce set of high quality real-time designs that can model more closely and easily the objective functions and tolerate the noisy evaluations given by these functions

bull The final design should be robust (ie performs well over a wide range of environment conditions) and flexible (ie allows easy adaptation after the environment has changed)

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

bull DMAs can successfully handle a mix of continuous and discrete parameters as well as selecting individual components from database Therefore another objective of this project is to connect DWN to a database which includes all the related information (physical geographicaland biochemical) colleted by other observation techniques (eg Geographic Information System (GIS) Remote Sensing (RS) photogrametery internet air (water and soil) survey technology etc)

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

DWN can effectively optimise this problem by 1) providing access to a wide range of data types in real-time 2) combining the observational data with innovative data analysis to improve forecasting and risk assessment 3) and being a user friendly tool in decision support of environmental management

Current Applications (Earthquake) (1)Current Applications (Earthquake) (1)

bull GEO-INFORMATION TECHNOLOGY AND ARTIFICIAL INTELLIGENCE FOR OPTIMIZING THE DISPLACEMENTS OF EARTHQUAKES A CASE STUDY FOR SYRIAN ACTIVE FAULT ZONES

bull With Dr Mohamad Rukieh Syrian Ministry of Commuincation and Technology

bull International Union of Geodesy and Geophysics IUGG07bull Perugia Italy July 3-14 2007bull httpwwwiugg2007perugiait

The Genetic Algorithms (GAs)The Genetic Algorithms (GAs) The evolution process for generating a populationThe evolution process for generating a population

Father

Mother

Mutation

SelectionMating

Child 1

Next Generation

Crossover

Initial Generation

N

Chromosome

Gene

One-flip mutation in child 2

Child 2

One- point crossover

Neural Networks NNNeural Networks NN imitate the human brain imitate the human brain

NN consists of a collection of interconnected simple computational units that work together cooperatively (like synapses in the human brain) These units are input units for receiving information from the problem domain hidden units as internal weight structure and output units for broadcasting data

Different types of the GPS Surveying Different types of the GPS Surveying NetworksNetworks

The Seychelles GPS Network (linear) The Malta GPS Network (triangulation)

Handicapped Person Transportation in the City of Handicapped Person Transportation in the City of Bruxelles using Grouping Genetic AlgorithmBruxelles using Grouping Genetic Algorithm

D1

D2

(n)

O2

O1

(m)

D

O

Depot1

Depot2

The covered area by Depot2The covered area by Depot 1

0

2

3 1

4

LocationDirection of vehicleContour of networkRegion of massif influx

[Ostarti =1000 Oendi =1015] [Dstarti =1100 Dendi =1130]

Desired departure interval=15 mins Desired arrival interval=30 mins

Travel time=0060 mins [1010 -1110]

1110 1010

Oi (home) Di (hospital)

Disaster Warning Network for Danube Disaster Warning Network for Danube Basin (DWN)Basin (DWN)

bull DWN is a system of satellites and ground stations for providing real time early warning of the impact of a disaster

bull DWN implements a network of Control Stations (CSs) spread over the whole geographic area of the hazard and transmits their observations to a master station

bull DWN provides this reliable information on a continuous basis through the parallel process of coverage accuracy prediction and integrity risk simulation functions

Disaster Warning Network Disaster Warning Network for the Danube Basinfor the Danube Basin

Reference Receiver RR

Roving Receiver 2

Roving Receiver 3

Roving Receiver 1

SatelliteGPS Signal 1

Error correction message 1

Roving Receiver 4

GPS Signal 4

GPS Signal RR

GPS Signal 2

GPS Signal 3

Error correction message 4

Error correction message 2

Error correction message 3

Differential GPSDifferential GPS

The DWN model for Environmental The DWN model for Environmental Pollution ControlPollution Control

bull Multiobjective Optimisation Problems (MOPs)

bull f(x) is the objective function to be optimised and X is the search domain

bull Ps is a multiplier for measuring the size of the user population near the location s (eg safety teams)bull f(acy ing) are the evaluations of accuracy coverage and integrity performance respectively for user location

sbull f(cost) is the cost function of a given DWN designbull N is the number of user locationsbull n is the number of obtained DWN designs

Xxxxxtosubject

xfxfxfxfoptimizeMOP

n

n

21

21

CostfffPnF n

N

s

sing

sacysDWN

1

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

bull Dynamic Metahuristics Algorithms (DMA) potentially have these capabilities to produce set of high quality real-time designs that can model more closely and easily the objective functions and tolerate the noisy evaluations given by these functions

bull The final design should be robust (ie performs well over a wide range of environment conditions) and flexible (ie allows easy adaptation after the environment has changed)

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

bull DMAs can successfully handle a mix of continuous and discrete parameters as well as selecting individual components from database Therefore another objective of this project is to connect DWN to a database which includes all the related information (physical geographicaland biochemical) colleted by other observation techniques (eg Geographic Information System (GIS) Remote Sensing (RS) photogrametery internet air (water and soil) survey technology etc)

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

DWN can effectively optimise this problem by 1) providing access to a wide range of data types in real-time 2) combining the observational data with innovative data analysis to improve forecasting and risk assessment 3) and being a user friendly tool in decision support of environmental management

Current Applications (Earthquake) (1)Current Applications (Earthquake) (1)

bull GEO-INFORMATION TECHNOLOGY AND ARTIFICIAL INTELLIGENCE FOR OPTIMIZING THE DISPLACEMENTS OF EARTHQUAKES A CASE STUDY FOR SYRIAN ACTIVE FAULT ZONES

bull With Dr Mohamad Rukieh Syrian Ministry of Commuincation and Technology

bull International Union of Geodesy and Geophysics IUGG07bull Perugia Italy July 3-14 2007bull httpwwwiugg2007perugiait

Neural Networks NNNeural Networks NN imitate the human brain imitate the human brain

NN consists of a collection of interconnected simple computational units that work together cooperatively (like synapses in the human brain) These units are input units for receiving information from the problem domain hidden units as internal weight structure and output units for broadcasting data

Different types of the GPS Surveying Different types of the GPS Surveying NetworksNetworks

The Seychelles GPS Network (linear) The Malta GPS Network (triangulation)

Handicapped Person Transportation in the City of Handicapped Person Transportation in the City of Bruxelles using Grouping Genetic AlgorithmBruxelles using Grouping Genetic Algorithm

D1

D2

(n)

O2

O1

(m)

D

O

Depot1

Depot2

The covered area by Depot2The covered area by Depot 1

0

2

3 1

4

LocationDirection of vehicleContour of networkRegion of massif influx

[Ostarti =1000 Oendi =1015] [Dstarti =1100 Dendi =1130]

Desired departure interval=15 mins Desired arrival interval=30 mins

Travel time=0060 mins [1010 -1110]

1110 1010

Oi (home) Di (hospital)

Disaster Warning Network for Danube Disaster Warning Network for Danube Basin (DWN)Basin (DWN)

bull DWN is a system of satellites and ground stations for providing real time early warning of the impact of a disaster

bull DWN implements a network of Control Stations (CSs) spread over the whole geographic area of the hazard and transmits their observations to a master station

bull DWN provides this reliable information on a continuous basis through the parallel process of coverage accuracy prediction and integrity risk simulation functions

Disaster Warning Network Disaster Warning Network for the Danube Basinfor the Danube Basin

Reference Receiver RR

Roving Receiver 2

Roving Receiver 3

Roving Receiver 1

SatelliteGPS Signal 1

Error correction message 1

Roving Receiver 4

GPS Signal 4

GPS Signal RR

GPS Signal 2

GPS Signal 3

Error correction message 4

Error correction message 2

Error correction message 3

Differential GPSDifferential GPS

The DWN model for Environmental The DWN model for Environmental Pollution ControlPollution Control

bull Multiobjective Optimisation Problems (MOPs)

bull f(x) is the objective function to be optimised and X is the search domain

bull Ps is a multiplier for measuring the size of the user population near the location s (eg safety teams)bull f(acy ing) are the evaluations of accuracy coverage and integrity performance respectively for user location

sbull f(cost) is the cost function of a given DWN designbull N is the number of user locationsbull n is the number of obtained DWN designs

Xxxxxtosubject

xfxfxfxfoptimizeMOP

n

n

21

21

CostfffPnF n

N

s

sing

sacysDWN

1

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

bull Dynamic Metahuristics Algorithms (DMA) potentially have these capabilities to produce set of high quality real-time designs that can model more closely and easily the objective functions and tolerate the noisy evaluations given by these functions

bull The final design should be robust (ie performs well over a wide range of environment conditions) and flexible (ie allows easy adaptation after the environment has changed)

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

bull DMAs can successfully handle a mix of continuous and discrete parameters as well as selecting individual components from database Therefore another objective of this project is to connect DWN to a database which includes all the related information (physical geographicaland biochemical) colleted by other observation techniques (eg Geographic Information System (GIS) Remote Sensing (RS) photogrametery internet air (water and soil) survey technology etc)

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

DWN can effectively optimise this problem by 1) providing access to a wide range of data types in real-time 2) combining the observational data with innovative data analysis to improve forecasting and risk assessment 3) and being a user friendly tool in decision support of environmental management

Current Applications (Earthquake) (1)Current Applications (Earthquake) (1)

bull GEO-INFORMATION TECHNOLOGY AND ARTIFICIAL INTELLIGENCE FOR OPTIMIZING THE DISPLACEMENTS OF EARTHQUAKES A CASE STUDY FOR SYRIAN ACTIVE FAULT ZONES

bull With Dr Mohamad Rukieh Syrian Ministry of Commuincation and Technology

bull International Union of Geodesy and Geophysics IUGG07bull Perugia Italy July 3-14 2007bull httpwwwiugg2007perugiait

Different types of the GPS Surveying Different types of the GPS Surveying NetworksNetworks

The Seychelles GPS Network (linear) The Malta GPS Network (triangulation)

Handicapped Person Transportation in the City of Handicapped Person Transportation in the City of Bruxelles using Grouping Genetic AlgorithmBruxelles using Grouping Genetic Algorithm

D1

D2

(n)

O2

O1

(m)

D

O

Depot1

Depot2

The covered area by Depot2The covered area by Depot 1

0

2

3 1

4

LocationDirection of vehicleContour of networkRegion of massif influx

[Ostarti =1000 Oendi =1015] [Dstarti =1100 Dendi =1130]

Desired departure interval=15 mins Desired arrival interval=30 mins

Travel time=0060 mins [1010 -1110]

1110 1010

Oi (home) Di (hospital)

Disaster Warning Network for Danube Disaster Warning Network for Danube Basin (DWN)Basin (DWN)

bull DWN is a system of satellites and ground stations for providing real time early warning of the impact of a disaster

bull DWN implements a network of Control Stations (CSs) spread over the whole geographic area of the hazard and transmits their observations to a master station

bull DWN provides this reliable information on a continuous basis through the parallel process of coverage accuracy prediction and integrity risk simulation functions

Disaster Warning Network Disaster Warning Network for the Danube Basinfor the Danube Basin

Reference Receiver RR

Roving Receiver 2

Roving Receiver 3

Roving Receiver 1

SatelliteGPS Signal 1

Error correction message 1

Roving Receiver 4

GPS Signal 4

GPS Signal RR

GPS Signal 2

GPS Signal 3

Error correction message 4

Error correction message 2

Error correction message 3

Differential GPSDifferential GPS

The DWN model for Environmental The DWN model for Environmental Pollution ControlPollution Control

bull Multiobjective Optimisation Problems (MOPs)

bull f(x) is the objective function to be optimised and X is the search domain

bull Ps is a multiplier for measuring the size of the user population near the location s (eg safety teams)bull f(acy ing) are the evaluations of accuracy coverage and integrity performance respectively for user location

sbull f(cost) is the cost function of a given DWN designbull N is the number of user locationsbull n is the number of obtained DWN designs

Xxxxxtosubject

xfxfxfxfoptimizeMOP

n

n

21

21

CostfffPnF n

N

s

sing

sacysDWN

1

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

bull Dynamic Metahuristics Algorithms (DMA) potentially have these capabilities to produce set of high quality real-time designs that can model more closely and easily the objective functions and tolerate the noisy evaluations given by these functions

bull The final design should be robust (ie performs well over a wide range of environment conditions) and flexible (ie allows easy adaptation after the environment has changed)

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

bull DMAs can successfully handle a mix of continuous and discrete parameters as well as selecting individual components from database Therefore another objective of this project is to connect DWN to a database which includes all the related information (physical geographicaland biochemical) colleted by other observation techniques (eg Geographic Information System (GIS) Remote Sensing (RS) photogrametery internet air (water and soil) survey technology etc)

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

DWN can effectively optimise this problem by 1) providing access to a wide range of data types in real-time 2) combining the observational data with innovative data analysis to improve forecasting and risk assessment 3) and being a user friendly tool in decision support of environmental management

Current Applications (Earthquake) (1)Current Applications (Earthquake) (1)

bull GEO-INFORMATION TECHNOLOGY AND ARTIFICIAL INTELLIGENCE FOR OPTIMIZING THE DISPLACEMENTS OF EARTHQUAKES A CASE STUDY FOR SYRIAN ACTIVE FAULT ZONES

bull With Dr Mohamad Rukieh Syrian Ministry of Commuincation and Technology

bull International Union of Geodesy and Geophysics IUGG07bull Perugia Italy July 3-14 2007bull httpwwwiugg2007perugiait

Handicapped Person Transportation in the City of Handicapped Person Transportation in the City of Bruxelles using Grouping Genetic AlgorithmBruxelles using Grouping Genetic Algorithm

D1

D2

(n)

O2

O1

(m)

D

O

Depot1

Depot2

The covered area by Depot2The covered area by Depot 1

0

2

3 1

4

LocationDirection of vehicleContour of networkRegion of massif influx

[Ostarti =1000 Oendi =1015] [Dstarti =1100 Dendi =1130]

Desired departure interval=15 mins Desired arrival interval=30 mins

Travel time=0060 mins [1010 -1110]

1110 1010

Oi (home) Di (hospital)

Disaster Warning Network for Danube Disaster Warning Network for Danube Basin (DWN)Basin (DWN)

bull DWN is a system of satellites and ground stations for providing real time early warning of the impact of a disaster

bull DWN implements a network of Control Stations (CSs) spread over the whole geographic area of the hazard and transmits their observations to a master station

bull DWN provides this reliable information on a continuous basis through the parallel process of coverage accuracy prediction and integrity risk simulation functions

Disaster Warning Network Disaster Warning Network for the Danube Basinfor the Danube Basin

Reference Receiver RR

Roving Receiver 2

Roving Receiver 3

Roving Receiver 1

SatelliteGPS Signal 1

Error correction message 1

Roving Receiver 4

GPS Signal 4

GPS Signal RR

GPS Signal 2

GPS Signal 3

Error correction message 4

Error correction message 2

Error correction message 3

Differential GPSDifferential GPS

The DWN model for Environmental The DWN model for Environmental Pollution ControlPollution Control

bull Multiobjective Optimisation Problems (MOPs)

bull f(x) is the objective function to be optimised and X is the search domain

bull Ps is a multiplier for measuring the size of the user population near the location s (eg safety teams)bull f(acy ing) are the evaluations of accuracy coverage and integrity performance respectively for user location

sbull f(cost) is the cost function of a given DWN designbull N is the number of user locationsbull n is the number of obtained DWN designs

Xxxxxtosubject

xfxfxfxfoptimizeMOP

n

n

21

21

CostfffPnF n

N

s

sing

sacysDWN

1

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

bull Dynamic Metahuristics Algorithms (DMA) potentially have these capabilities to produce set of high quality real-time designs that can model more closely and easily the objective functions and tolerate the noisy evaluations given by these functions

bull The final design should be robust (ie performs well over a wide range of environment conditions) and flexible (ie allows easy adaptation after the environment has changed)

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

bull DMAs can successfully handle a mix of continuous and discrete parameters as well as selecting individual components from database Therefore another objective of this project is to connect DWN to a database which includes all the related information (physical geographicaland biochemical) colleted by other observation techniques (eg Geographic Information System (GIS) Remote Sensing (RS) photogrametery internet air (water and soil) survey technology etc)

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

DWN can effectively optimise this problem by 1) providing access to a wide range of data types in real-time 2) combining the observational data with innovative data analysis to improve forecasting and risk assessment 3) and being a user friendly tool in decision support of environmental management

Current Applications (Earthquake) (1)Current Applications (Earthquake) (1)

bull GEO-INFORMATION TECHNOLOGY AND ARTIFICIAL INTELLIGENCE FOR OPTIMIZING THE DISPLACEMENTS OF EARTHQUAKES A CASE STUDY FOR SYRIAN ACTIVE FAULT ZONES

bull With Dr Mohamad Rukieh Syrian Ministry of Commuincation and Technology

bull International Union of Geodesy and Geophysics IUGG07bull Perugia Italy July 3-14 2007bull httpwwwiugg2007perugiait

Disaster Warning Network for Danube Disaster Warning Network for Danube Basin (DWN)Basin (DWN)

bull DWN is a system of satellites and ground stations for providing real time early warning of the impact of a disaster

bull DWN implements a network of Control Stations (CSs) spread over the whole geographic area of the hazard and transmits their observations to a master station

bull DWN provides this reliable information on a continuous basis through the parallel process of coverage accuracy prediction and integrity risk simulation functions

Disaster Warning Network Disaster Warning Network for the Danube Basinfor the Danube Basin

Reference Receiver RR

Roving Receiver 2

Roving Receiver 3

Roving Receiver 1

SatelliteGPS Signal 1

Error correction message 1

Roving Receiver 4

GPS Signal 4

GPS Signal RR

GPS Signal 2

GPS Signal 3

Error correction message 4

Error correction message 2

Error correction message 3

Differential GPSDifferential GPS

The DWN model for Environmental The DWN model for Environmental Pollution ControlPollution Control

bull Multiobjective Optimisation Problems (MOPs)

bull f(x) is the objective function to be optimised and X is the search domain

bull Ps is a multiplier for measuring the size of the user population near the location s (eg safety teams)bull f(acy ing) are the evaluations of accuracy coverage and integrity performance respectively for user location

sbull f(cost) is the cost function of a given DWN designbull N is the number of user locationsbull n is the number of obtained DWN designs

Xxxxxtosubject

xfxfxfxfoptimizeMOP

n

n

21

21

CostfffPnF n

N

s

sing

sacysDWN

1

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

bull Dynamic Metahuristics Algorithms (DMA) potentially have these capabilities to produce set of high quality real-time designs that can model more closely and easily the objective functions and tolerate the noisy evaluations given by these functions

bull The final design should be robust (ie performs well over a wide range of environment conditions) and flexible (ie allows easy adaptation after the environment has changed)

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

bull DMAs can successfully handle a mix of continuous and discrete parameters as well as selecting individual components from database Therefore another objective of this project is to connect DWN to a database which includes all the related information (physical geographicaland biochemical) colleted by other observation techniques (eg Geographic Information System (GIS) Remote Sensing (RS) photogrametery internet air (water and soil) survey technology etc)

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

DWN can effectively optimise this problem by 1) providing access to a wide range of data types in real-time 2) combining the observational data with innovative data analysis to improve forecasting and risk assessment 3) and being a user friendly tool in decision support of environmental management

Current Applications (Earthquake) (1)Current Applications (Earthquake) (1)

bull GEO-INFORMATION TECHNOLOGY AND ARTIFICIAL INTELLIGENCE FOR OPTIMIZING THE DISPLACEMENTS OF EARTHQUAKES A CASE STUDY FOR SYRIAN ACTIVE FAULT ZONES

bull With Dr Mohamad Rukieh Syrian Ministry of Commuincation and Technology

bull International Union of Geodesy and Geophysics IUGG07bull Perugia Italy July 3-14 2007bull httpwwwiugg2007perugiait

Disaster Warning Network Disaster Warning Network for the Danube Basinfor the Danube Basin

Reference Receiver RR

Roving Receiver 2

Roving Receiver 3

Roving Receiver 1

SatelliteGPS Signal 1

Error correction message 1

Roving Receiver 4

GPS Signal 4

GPS Signal RR

GPS Signal 2

GPS Signal 3

Error correction message 4

Error correction message 2

Error correction message 3

Differential GPSDifferential GPS

The DWN model for Environmental The DWN model for Environmental Pollution ControlPollution Control

bull Multiobjective Optimisation Problems (MOPs)

bull f(x) is the objective function to be optimised and X is the search domain

bull Ps is a multiplier for measuring the size of the user population near the location s (eg safety teams)bull f(acy ing) are the evaluations of accuracy coverage and integrity performance respectively for user location

sbull f(cost) is the cost function of a given DWN designbull N is the number of user locationsbull n is the number of obtained DWN designs

Xxxxxtosubject

xfxfxfxfoptimizeMOP

n

n

21

21

CostfffPnF n

N

s

sing

sacysDWN

1

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

bull Dynamic Metahuristics Algorithms (DMA) potentially have these capabilities to produce set of high quality real-time designs that can model more closely and easily the objective functions and tolerate the noisy evaluations given by these functions

bull The final design should be robust (ie performs well over a wide range of environment conditions) and flexible (ie allows easy adaptation after the environment has changed)

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

bull DMAs can successfully handle a mix of continuous and discrete parameters as well as selecting individual components from database Therefore another objective of this project is to connect DWN to a database which includes all the related information (physical geographicaland biochemical) colleted by other observation techniques (eg Geographic Information System (GIS) Remote Sensing (RS) photogrametery internet air (water and soil) survey technology etc)

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

DWN can effectively optimise this problem by 1) providing access to a wide range of data types in real-time 2) combining the observational data with innovative data analysis to improve forecasting and risk assessment 3) and being a user friendly tool in decision support of environmental management

Current Applications (Earthquake) (1)Current Applications (Earthquake) (1)

bull GEO-INFORMATION TECHNOLOGY AND ARTIFICIAL INTELLIGENCE FOR OPTIMIZING THE DISPLACEMENTS OF EARTHQUAKES A CASE STUDY FOR SYRIAN ACTIVE FAULT ZONES

bull With Dr Mohamad Rukieh Syrian Ministry of Commuincation and Technology

bull International Union of Geodesy and Geophysics IUGG07bull Perugia Italy July 3-14 2007bull httpwwwiugg2007perugiait

The DWN model for Environmental The DWN model for Environmental Pollution ControlPollution Control

bull Multiobjective Optimisation Problems (MOPs)

bull f(x) is the objective function to be optimised and X is the search domain

bull Ps is a multiplier for measuring the size of the user population near the location s (eg safety teams)bull f(acy ing) are the evaluations of accuracy coverage and integrity performance respectively for user location

sbull f(cost) is the cost function of a given DWN designbull N is the number of user locationsbull n is the number of obtained DWN designs

Xxxxxtosubject

xfxfxfxfoptimizeMOP

n

n

21

21

CostfffPnF n

N

s

sing

sacysDWN

1

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

bull Dynamic Metahuristics Algorithms (DMA) potentially have these capabilities to produce set of high quality real-time designs that can model more closely and easily the objective functions and tolerate the noisy evaluations given by these functions

bull The final design should be robust (ie performs well over a wide range of environment conditions) and flexible (ie allows easy adaptation after the environment has changed)

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

bull DMAs can successfully handle a mix of continuous and discrete parameters as well as selecting individual components from database Therefore another objective of this project is to connect DWN to a database which includes all the related information (physical geographicaland biochemical) colleted by other observation techniques (eg Geographic Information System (GIS) Remote Sensing (RS) photogrametery internet air (water and soil) survey technology etc)

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

DWN can effectively optimise this problem by 1) providing access to a wide range of data types in real-time 2) combining the observational data with innovative data analysis to improve forecasting and risk assessment 3) and being a user friendly tool in decision support of environmental management

Current Applications (Earthquake) (1)Current Applications (Earthquake) (1)

bull GEO-INFORMATION TECHNOLOGY AND ARTIFICIAL INTELLIGENCE FOR OPTIMIZING THE DISPLACEMENTS OF EARTHQUAKES A CASE STUDY FOR SYRIAN ACTIVE FAULT ZONES

bull With Dr Mohamad Rukieh Syrian Ministry of Commuincation and Technology

bull International Union of Geodesy and Geophysics IUGG07bull Perugia Italy July 3-14 2007bull httpwwwiugg2007perugiait

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

bull Dynamic Metahuristics Algorithms (DMA) potentially have these capabilities to produce set of high quality real-time designs that can model more closely and easily the objective functions and tolerate the noisy evaluations given by these functions

bull The final design should be robust (ie performs well over a wide range of environment conditions) and flexible (ie allows easy adaptation after the environment has changed)

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

bull DMAs can successfully handle a mix of continuous and discrete parameters as well as selecting individual components from database Therefore another objective of this project is to connect DWN to a database which includes all the related information (physical geographicaland biochemical) colleted by other observation techniques (eg Geographic Information System (GIS) Remote Sensing (RS) photogrametery internet air (water and soil) survey technology etc)

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

DWN can effectively optimise this problem by 1) providing access to a wide range of data types in real-time 2) combining the observational data with innovative data analysis to improve forecasting and risk assessment 3) and being a user friendly tool in decision support of environmental management

Current Applications (Earthquake) (1)Current Applications (Earthquake) (1)

bull GEO-INFORMATION TECHNOLOGY AND ARTIFICIAL INTELLIGENCE FOR OPTIMIZING THE DISPLACEMENTS OF EARTHQUAKES A CASE STUDY FOR SYRIAN ACTIVE FAULT ZONES

bull With Dr Mohamad Rukieh Syrian Ministry of Commuincation and Technology

bull International Union of Geodesy and Geophysics IUGG07bull Perugia Italy July 3-14 2007bull httpwwwiugg2007perugiait

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

bull DMAs can successfully handle a mix of continuous and discrete parameters as well as selecting individual components from database Therefore another objective of this project is to connect DWN to a database which includes all the related information (physical geographicaland biochemical) colleted by other observation techniques (eg Geographic Information System (GIS) Remote Sensing (RS) photogrametery internet air (water and soil) survey technology etc)

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

DWN can effectively optimise this problem by 1) providing access to a wide range of data types in real-time 2) combining the observational data with innovative data analysis to improve forecasting and risk assessment 3) and being a user friendly tool in decision support of environmental management

Current Applications (Earthquake) (1)Current Applications (Earthquake) (1)

bull GEO-INFORMATION TECHNOLOGY AND ARTIFICIAL INTELLIGENCE FOR OPTIMIZING THE DISPLACEMENTS OF EARTHQUAKES A CASE STUDY FOR SYRIAN ACTIVE FAULT ZONES

bull With Dr Mohamad Rukieh Syrian Ministry of Commuincation and Technology

bull International Union of Geodesy and Geophysics IUGG07bull Perugia Italy July 3-14 2007bull httpwwwiugg2007perugiait

Disaster Warning Network (DWN)Disaster Warning Network (DWN)

DWN can effectively optimise this problem by 1) providing access to a wide range of data types in real-time 2) combining the observational data with innovative data analysis to improve forecasting and risk assessment 3) and being a user friendly tool in decision support of environmental management

Current Applications (Earthquake) (1)Current Applications (Earthquake) (1)

bull GEO-INFORMATION TECHNOLOGY AND ARTIFICIAL INTELLIGENCE FOR OPTIMIZING THE DISPLACEMENTS OF EARTHQUAKES A CASE STUDY FOR SYRIAN ACTIVE FAULT ZONES

bull With Dr Mohamad Rukieh Syrian Ministry of Commuincation and Technology

bull International Union of Geodesy and Geophysics IUGG07bull Perugia Italy July 3-14 2007bull httpwwwiugg2007perugiait

Current Applications (Earthquake) (1)Current Applications (Earthquake) (1)

bull GEO-INFORMATION TECHNOLOGY AND ARTIFICIAL INTELLIGENCE FOR OPTIMIZING THE DISPLACEMENTS OF EARTHQUAKES A CASE STUDY FOR SYRIAN ACTIVE FAULT ZONES

bull With Dr Mohamad Rukieh Syrian Ministry of Commuincation and Technology

bull International Union of Geodesy and Geophysics IUGG07bull Perugia Italy July 3-14 2007bull httpwwwiugg2007perugiait

Current Applications (Flooding) (2)Current Applications (Flooding) (2)

bull Intelligent Decision Support System based Metaheuristics and Spatial Planning for Flood Management in Flanders Belgium

bull With Wouter VANNEUVILLE (Flandes Hydraulics Research) Georges ALLAERT Philippe DE MAEYER (Ghent University)

bull The 4th International Conference on Information Systems forCrisis Response and Management

bull Special Session on SPATIAL PLANNING (SP) FOR DISASTER RISK REDUCTION (DRR)

bull Delft the Netherlands May 13-16 2007bull httpwwwiscramorg

Conclusion amp Future WorkConclusion amp Future Work

bull The most significant development in these recent years for disaster management lies in the better integration of GNSS image processing and GIS systems coupled with intelligent algorithms

Outlookmore robust models

Early Warning Challenges hellipbull Warnings Risks are NOT Understoodbull Information is Scatteredbull Dissemination is Limitedbull ldquoHigh Availabilityrdquo and Redundancy are Essentialbull Time is LIFE

bull The Joint Regional Conference On Disaster Relief and Management International Cooperation amp Role of ICT

bull 14-17 April 2007 bull Alexandriabull Egypt

Innovative Decision Support System based Artificial Intelligence amp Spatial Innovative Decision Support System based Artificial Intelligence amp Spatial Planning for Disaster Risk ReductionPlanning for Disaster Risk Reduction

Session VISession VI Reduction of Risk in Environmental Disaster Reduction of Risk in Environmental Disaster

Dr Eng Hussain Aziz SALEH

Ministry of Local Administration amp Environment Damascus Syria

Tel 00 963 11 211 9955

Fax 00 963 11 211 9954

httpiridiaulbacbe~hsaleh

HussainSalehUGentbe

Syrian Ministry of

Higher Education

• Innovative Decision Support System based Artificial Intelligence amp Spatial Planning for Disaster Risk Reduction Session VI Reduction of Risk in Environmental Disaster
• Contents of the Paper
• Slide 19
• Slide 20
• Slide 21
• Slide 22
• Global Navigation Satellite Systems GNSSs
• Slide 24
• Slide 25
• Slide 26
• Multiobjective Combinatorial Optimisation Problems (MCOPs)
• Maximisation (Global and local maxima)
• Schematic Representation of the Search Progress of Metaheuristic Techniques
• Metaheuristic Techniques
• The Simulated Annealing (SA) Technique
• Slide 32
• Slide 33
• ACO
• Slide 35
• Neural Networks NN imitate the human brain
• Different types of the GPS Surveying Networks
• Handicapped Person Transportation in the City of Bruxelles using Grouping Genetic Algorithm
• Disaster Warning Network for Danube Basin (DWN)
• Disaster Warning Network for the Danube Basin
• The DWN model for Environmental Pollution Control
• Disaster Warning Network (DWN)
• Slide 27
• Slide 28
• Current Applications (Earthquake) (1)
• Current Applications (Flooding) (2)
• Conclusion amp Future Work

Conclusion amp Future WorkConclusion amp Future Work

bull The most significant development in these recent years for disaster management lies in the better integration of GNSS image processing and GIS systems coupled with intelligent algorithms

Outlookmore robust models

Early Warning Challenges hellipbull Warnings Risks are NOT Understoodbull Information is Scatteredbull Dissemination is Limitedbull ldquoHigh Availabilityrdquo and Redundancy are Essentialbull Time is LIFE

bull The Joint Regional Conference On Disaster Relief and Management International Cooperation amp Role of ICT

bull 14-17 April 2007 bull Alexandriabull Egypt

Innovative Decision Support System based Artificial Intelligence amp Spatial Innovative Decision Support System based Artificial Intelligence amp Spatial Planning for Disaster Risk ReductionPlanning for Disaster Risk Reduction

Session VISession VI Reduction of Risk in Environmental Disaster Reduction of Risk in Environmental Disaster

Dr Eng Hussain Aziz SALEH

Ministry of Local Administration amp Environment Damascus Syria

Tel 00 963 11 211 9955

Fax 00 963 11 211 9954

httpiridiaulbacbe~hsaleh

HussainSalehUGentbe

Syrian Ministry of

Higher Education

• Innovative Decision Support System based Artificial Intelligence amp Spatial Planning for Disaster Risk Reduction Session VI Reduction of Risk in Environmental Disaster
• Contents of the Paper
• Slide 19
• Slide 20
• Slide 21
• Slide 22
• Global Navigation Satellite Systems GNSSs
• Slide 24
• Slide 25
• Slide 26
• Multiobjective Combinatorial Optimisation Problems (MCOPs)
• Maximisation (Global and local maxima)
• Schematic Representation of the Search Progress of Metaheuristic Techniques
• Metaheuristic Techniques
• The Simulated Annealing (SA) Technique
• Slide 32
• Slide 33
• ACO
• Slide 35
• Neural Networks NN imitate the human brain
• Different types of the GPS Surveying Networks
• Handicapped Person Transportation in the City of Bruxelles using Grouping Genetic Algorithm
• Disaster Warning Network for Danube Basin (DWN)
• Disaster Warning Network for the Danube Basin
• The DWN model for Environmental Pollution Control
• Disaster Warning Network (DWN)
• Slide 27
• Slide 28
• Current Applications (Earthquake) (1)
• Current Applications (Flooding) (2)
• Conclusion amp Future Work

bull The Joint Regional Conference On Disaster Relief and Management International Cooperation amp Role of ICT

bull 14-17 April 2007 bull Alexandriabull Egypt

Innovative Decision Support System based Artificial Intelligence amp Spatial Innovative Decision Support System based Artificial Intelligence amp Spatial Planning for Disaster Risk ReductionPlanning for Disaster Risk Reduction

Session VISession VI Reduction of Risk in Environmental Disaster Reduction of Risk in Environmental Disaster

Dr Eng Hussain Aziz SALEH

Ministry of Local Administration amp Environment Damascus Syria

Tel 00 963 11 211 9955

Fax 00 963 11 211 9954

httpiridiaulbacbe~hsaleh

HussainSalehUGentbe

Syrian Ministry of

Higher Education

• Innovative Decision Support System based Artificial Intelligence amp Spatial Planning for Disaster Risk Reduction Session VI Reduction of Risk in Environmental Disaster
• Contents of the Paper
• Slide 19
• Slide 20
• Slide 21
• Slide 22
• Global Navigation Satellite Systems GNSSs
• Slide 24
• Slide 25
• Slide 26
• Multiobjective Combinatorial Optimisation Problems (MCOPs)
• Maximisation (Global and local maxima)
• Schematic Representation of the Search Progress of Metaheuristic Techniques
• Metaheuristic Techniques
• The Simulated Annealing (SA) Technique
• Slide 32
• Slide 33
• ACO
• Slide 35
• Neural Networks NN imitate the human brain
• Different types of the GPS Surveying Networks
• Handicapped Person Transportation in the City of Bruxelles using Grouping Genetic Algorithm
• Disaster Warning Network for Danube Basin (DWN)
• Disaster Warning Network for the Danube Basin
• The DWN model for Environmental Pollution Control
• Disaster Warning Network (DWN)
• Slide 27
• Slide 28
• Current Applications (Earthquake) (1)
• Current Applications (Flooding) (2)
• Conclusion amp Future Work