SMART INCO-MEDSMART INCO-MEDkick-off meeting,kick-off meeting,January 5/6 2003January 5/6 2003CEDARE, CAIROCEDARE, CAIRO
SMART INCO-MEDSMART INCO-MEDkick-off meeting,kick-off meeting,January 5/6 2003January 5/6 2003CEDARE, CAIROCEDARE, CAIRO
DDr. Kurt Fedra ESS GmbH, [email protected] http://www.ess.co.atEnvironmental Software & Services A-2352 Gumpoldskirchen
DDr. Kurt Fedra ESS GmbH, [email protected] http://www.ess.co.atEnvironmental Software & Services A-2352 Gumpoldskirchen
SMART: Project Overview SMART: Project Overview
• 3 year duration to August 2005• Started: September 2002 • Current PM: 5• 9 partners and countries• 12 work packages• 5 case studies:
TR,LB,JO,EG,TU
• 3 year duration to August 2005• Started: September 2002 • Current PM: 5• 9 partners and countries• 12 work packages• 5 case studies:
TR,LB,JO,EG,TU
SMART: Objectives SMART: Objectives Develop policy guidelines for ICZM,
emphasis on water resources• Conflicting water use• Resource economics• Quantitative analysis using indicators,
models, and expert systems• Public information, Internet• Case studies, collaborative network
within and between countries
Develop policy guidelines for ICZM, emphasis on water resources
• Conflicting water use• Resource economics• Quantitative analysis using indicators,
models, and expert systems• Public information, Internet• Case studies, collaborative network
within and between countries
SMART: Technical Objectives SMART: Technical Objectives
1. Model integration, linkage through expert systems technology
2. Linkage of models and aggregate policy level indicators
3. Linkage of models and public information (Internet)
1. Model integration, linkage through expert systems technology
2. Linkage of models and aggregate policy level indicators
3. Linkage of models and public information (Internet)
SMART: Work Plan PhasesSMART: Work Plan Phases
1. Requirements analysis, data availability, specifications
2. Data compilation, tool development
3. Parallel case studies4. Comparative evaluation,
dissemination.
1. Requirements analysis, data availability, specifications
2. Data compilation, tool development
3. Parallel case studies4. Comparative evaluation,
dissemination.
SMART: Milestones SMART: Milestones
1 PM 09 End of preparatory phase, first workshop
2 PM 12 Methods and tools prototypes ready, start of operational phase
3 PM 18 Case studies implemented, first results of scenario analysis
4 PM 24 Analysis and assessment phase initiated
5 PM 30 Case studies completed, final comparative analysis
6 PM36 Project and reporting completed
1 PM 09 End of preparatory phase, first workshop
2 PM 12 Methods and tools prototypes ready, start of operational phase
3 PM 18 Case studies implemented, first results of scenario analysis
4 PM 24 Analysis and assessment phase initiated
5 PM 30 Case studies completed, final comparative analysis
6 PM36 Project and reporting completed
SMART: work packages SMART: work packages
WP 0: Coordination and AdministrationESS, PM 1-36Communication:
Mailing list: [email protected] server www.ess.co.at/SMARTDiscussion board:Meetings:
Output:Reports:Deliverables:Cost statements:Project Review:
WP 0: Coordination and AdministrationESS, PM 1-36Communication:
Mailing list: [email protected] server www.ess.co.at/SMARTDiscussion board:Meetings:
Output:Reports:Deliverables:Cost statements:Project Review:
SMART: work packages SMART: work packages
WP 01: Requirements and constraints analysis
FEEM, PM 1-6
Deliverable due by
February 2003 !
WP 01: Requirements and constraints analysis
FEEM, PM 1-6
Deliverable due by
February 2003 !
SMART: work packages SMART: work packages
WP 02: Socio-economic framework and guidelines
UATLA, PM 3-12
WP 02: Socio-economic framework and guidelines
UATLA, PM 3-12
SMART: work packages SMART: work packages
WP 03: Analytical tools, models
SOGREAH, PM 3-18
Subtasks for
• TELEMAC
• WaterWare, XPS
WP 03: Analytical tools, models
SOGREAH, PM 3-18
Subtasks for
• TELEMAC
• WaterWare, XPS
SMART: work packages SMART: work packages
WP 04: Data compilation and analysis
TR, PM 6-24
Includes parallel sub-tasks, one for each case study/country
WP 04: Data compilation and analysis
TR, PM 6-24
Includes parallel sub-tasks, one for each case study/country
SMART: WP04 SMART: WP04
1. Develop meta-data structure2. Formats, technical specifications,3. Coverage and resolution (space
and time)4. Develop checklists5. Monitor compilation6. Comparative analysis
(completeness, consistency, plausibility)
1. Develop meta-data structure2. Formats, technical specifications,3. Coverage and resolution (space
and time)4. Develop checklists5. Monitor compilation6. Comparative analysis
(completeness, consistency, plausibility)
SMART: WP04 SMART: WP04
Common data base or data repository;Extensive documentation !Accessible from ftp server at project
web siteSelected data available with interactive
on-line tools (e.g., hydro- meteorological time series data)
Map server at CEDARE
Common data base or data repository;Extensive documentation !Accessible from ftp server at project
web siteSelected data available with interactive
on-line tools (e.g., hydro- meteorological time series data)
Map server at CEDARE
SMART: work packages SMART: work packages
WP 05 – 09 Case Studies
Respective partner, PM 12-30,
Overlaps with data compilation
WP 05 – 09 Case Studies
Respective partner, PM 12-30,
Overlaps with data compilation
SMART: work packages SMART: work packages
WP 10: Comparative analysis
FEEM, PM 24-36
Requires input from all case studies
WP 10: Comparative analysis
FEEM, PM 24-36
Requires input from all case studies
SMART: work packages SMART: work packages
WP 11: Dissemination and exploitation
ESS, PM 3-36
WP 11: Dissemination and exploitation
ESS, PM 3-36
SMART: time tableSMART: time table
SMART SMART
Water managementWater management
must be analyzed in a broad must be analyzed in a broad systems context:systems context:– Socio-economic aspectsSocio-economic aspects (costs and (costs and
benefits, jobs, institutions, regulations)benefits, jobs, institutions, regulations)
– Environmental aspectsEnvironmental aspects (water (water quality, water allocation, alternative use)quality, water allocation, alternative use)
– Technological aspectsTechnological aspects (constraints,BAT, clean technologies, (constraints,BAT, clean technologies, water efficiency, reuse and recycling)water efficiency, reuse and recycling)
must be analyzed in a broad must be analyzed in a broad systems context:systems context:– Socio-economic aspectsSocio-economic aspects (costs and (costs and
benefits, jobs, institutions, regulations)benefits, jobs, institutions, regulations)
– Environmental aspectsEnvironmental aspects (water (water quality, water allocation, alternative use)quality, water allocation, alternative use)
– Technological aspectsTechnological aspects (constraints,BAT, clean technologies, (constraints,BAT, clean technologies, water efficiency, reuse and recycling)water efficiency, reuse and recycling)
Water managementWater management
Conflicting water use and changing, Conflicting water use and changing, stochastic constraintsstochastic constraints
• Multiple criteria, conflicting objectivesMultiple criteria, conflicting objectives
• Industrial water management:Industrial water management:– Water demandWater demand
• Consumptive useConsumptive use
• Water pollutionWater pollution
Conflicting water use and changing, Conflicting water use and changing, stochastic constraintsstochastic constraints
• Multiple criteria, conflicting objectivesMultiple criteria, conflicting objectives
• Industrial water management:Industrial water management:– Water demandWater demand
• Consumptive useConsumptive use
• Water pollutionWater pollution
Environmental problemsEnvironmental problems
Water Management problems:Water Management problems:– Not enoughNot enough– Too muchToo much– At the wrong place At the wrong place – At the wrong timeAt the wrong time– Insufficient qualityInsufficient quality
Problems of distribution of Problems of distribution of resources resources (clean air, water, land, (clean air, water, land, biodiversity, …)biodiversity, …)
Water Management problems:Water Management problems:– Not enoughNot enough– Too muchToo much– At the wrong place At the wrong place – At the wrong timeAt the wrong time– Insufficient qualityInsufficient quality
Problems of distribution of Problems of distribution of resources resources (clean air, water, land, (clean air, water, land, biodiversity, …)biodiversity, …)
Environmental problemsEnvironmental problems
result from the local or short-term result from the local or short-term optimization of resource optimization of resource management strategies, ignoring management strategies, ignoring some externalities (side effects, some externalities (side effects, costs to others).costs to others).
All life degrades its environment.All life degrades its environment.
All living systems have self-regulatory All living systems have self-regulatory capabilities – within usually unknown capabilities – within usually unknown
limits.limits.
result from the local or short-term result from the local or short-term optimization of resource optimization of resource management strategies, ignoring management strategies, ignoring some externalities (side effects, some externalities (side effects, costs to others).costs to others).
All life degrades its environment.All life degrades its environment.
All living systems have self-regulatory All living systems have self-regulatory capabilities – within usually unknown capabilities – within usually unknown
limits.limits.
Environmental problemsEnvironmental problems
Increasing human populationIncreasing human population
Increasing resource consumptionIncreasing resource consumption– EnergyEnergy– MaterialsMaterials– SpaceSpace
And potentially irreversible And potentially irreversible destruction of information destruction of information (biodiversity)(biodiversity)
Increasing human populationIncreasing human population
Increasing resource consumptionIncreasing resource consumption– EnergyEnergy– MaterialsMaterials– SpaceSpace
And potentially irreversible And potentially irreversible destruction of information destruction of information (biodiversity)(biodiversity)
Environmental problemsEnvironmental problems
Three laws of ecology:Three laws of ecology:
1.1. Everything is connected to Everything is connected to everything else;everything else;
2.2. Everything must go somewhere;Everything must go somewhere;
3.3. Nature knows best.Nature knows best. Barry Commoner, Barry Commoner,
The Closing The Closing Cycle.Cycle.
Three laws of ecology:Three laws of ecology:
1.1. Everything is connected to Everything is connected to everything else;everything else;
2.2. Everything must go somewhere;Everything must go somewhere;
3.3. Nature knows best.Nature knows best. Barry Commoner, Barry Commoner,
The Closing The Closing Cycle.Cycle.
Environmental problemsEnvironmental problems
Root problem:
Uncoupling of feedback loops
(to obtain local or short-term benefits)•Tragedy of the Commons (Hardin,
1968)
•Social costs (Kapp, 1979)
•Limits to Growth (Meadows et al., 1971)
•Malthus (1830)
Root problem:
Uncoupling of feedback loops
(to obtain local or short-term benefits)•Tragedy of the Commons (Hardin,
1968)
•Social costs (Kapp, 1979)
•Limits to Growth (Meadows et al., 1971)
•Malthus (1830)
Environmental problemsEnvironmental problems
IF: quantity or quality, spatial or IF: quantity or quality, spatial or temporal distribution of temporal distribution of environmental resources do not environmental resources do not match our needs or expectations:match our needs or expectations:– EnvironmentEnvironment (objective reality)(objective reality)– Needs Needs (objective-subjective (objective-subjective
reality)reality)– ExpectationsExpectations (subjective reality)(subjective reality)
IF: quantity or quality, spatial or IF: quantity or quality, spatial or temporal distribution of temporal distribution of environmental resources do not environmental resources do not match our needs or expectations:match our needs or expectations:– EnvironmentEnvironment (objective reality)(objective reality)– Needs Needs (objective-subjective (objective-subjective
reality)reality)– ExpectationsExpectations (subjective reality)(subjective reality)
Regulatory responseRegulatory response
Laws and regulations:Laws and regulations:• Emission control (water, air)Emission control (water, air)• Product standards Product standards (fuel, engines, (fuel, engines,
BAT)BAT)
• Permitting, zoningPermitting, zoning• Monetary instruments:Monetary instruments:
– Taxes (waste tax)Taxes (waste tax)– Subsidies (for mitigation)Subsidies (for mitigation)
Laws and regulations:Laws and regulations:• Emission control (water, air)Emission control (water, air)• Product standards Product standards (fuel, engines, (fuel, engines,
BAT)BAT)
• Permitting, zoningPermitting, zoning• Monetary instruments:Monetary instruments:
– Taxes (waste tax)Taxes (waste tax)– Subsidies (for mitigation)Subsidies (for mitigation)
Regulatory responseRegulatory response
Planning requirements:Planning requirements:– Environmental impact assessmentEnvironmental impact assessment– Risk assessmentRisk assessment
Self-regulation:Self-regulation:– ISO 14000, 9000ISO 14000, 9000– EMAS, Eco-AuditEMAS, Eco-Audit– Responsible CareResponsible Care– Labeling (“biological” food)Labeling (“biological” food)
Planning requirements:Planning requirements:– Environmental impact assessmentEnvironmental impact assessment– Risk assessmentRisk assessment
Self-regulation:Self-regulation:– ISO 14000, 9000ISO 14000, 9000– EMAS, Eco-AuditEMAS, Eco-Audit– Responsible CareResponsible Care– Labeling (“biological” food)Labeling (“biological” food)
Water management problemsWater management problems
are inherently multi-disciplinary:are inherently multi-disciplinary:• Hydrology, geology, climatology, Hydrology, geology, climatology,
geographygeography• (Geo)physics, chemistry(Geo)physics, chemistry• Biology, ecology, toxicologyBiology, ecology, toxicology• Engineering, economicsEngineering, economics• Psychology, sociologyPsychology, sociology• Law, political sciencesLaw, political sciences
are inherently multi-disciplinary:are inherently multi-disciplinary:• Hydrology, geology, climatology, Hydrology, geology, climatology,
geographygeography• (Geo)physics, chemistry(Geo)physics, chemistry• Biology, ecology, toxicologyBiology, ecology, toxicology• Engineering, economicsEngineering, economics• Psychology, sociologyPsychology, sociology• Law, political sciencesLaw, political sciences
Water management problemsWater management problems
• are complex (many elements and are complex (many elements and interactions)interactions)
• dynamic (including delay, memory)dynamic (including delay, memory)• spatially distributed (1, 1.5, 2 and 3D) spatially distributed (1, 1.5, 2 and 3D) • non-linear (feedback, bifurcation, etc.)non-linear (feedback, bifurcation, etc.)• involve large uncertainties ininvolve large uncertainties in
- the physical domain- the physical domain
- the socio-economic domain - the socio-economic domain • involve multiple actors and stake holders involve multiple actors and stake holders • are always multi-criteria, multi-objectiveare always multi-criteria, multi-objective
• are complex (many elements and are complex (many elements and interactions)interactions)
• dynamic (including delay, memory)dynamic (including delay, memory)• spatially distributed (1, 1.5, 2 and 3D) spatially distributed (1, 1.5, 2 and 3D) • non-linear (feedback, bifurcation, etc.)non-linear (feedback, bifurcation, etc.)• involve large uncertainties ininvolve large uncertainties in
- the physical domain- the physical domain
- the socio-economic domain - the socio-economic domain • involve multiple actors and stake holders involve multiple actors and stake holders • are always multi-criteria, multi-objectiveare always multi-criteria, multi-objective
A river basin perspective:A river basin perspective:
Water can easily be accounted for, a mass Water can easily be accounted for, a mass budget approach is feasible;budget approach is feasible;
The hydrographic unit of the catchment or The hydrographic unit of the catchment or river basin provides a naturally bounded river basin provides a naturally bounded well defined system;well defined system;
Conservation laws (mass, momentum) are Conservation laws (mass, momentum) are used to describe dynamic water budgets.used to describe dynamic water budgets.
A river basin perspective:A river basin perspective:
Industrial water use is one of the demand Industrial water use is one of the demand nodes in a river basin network/graph:nodes in a river basin network/graph:– Input nodes (sub-catchment, wells)Input nodes (sub-catchment, wells)– Domestic demand nodesDomestic demand nodes– Agricultural demand nodesAgricultural demand nodes– Industrial demand nodesIndustrial demand nodes– Reservoirs, lakesReservoirs, lakes– Structural components (confluence)Structural components (confluence)
connected by river reaches, canalsconnected by river reaches, canals
Water demandWater demand
Depends on:Depends on:• Production volumeProduction volume• Production technologyProduction technology• Recycling strategiesRecycling strategies
Demand has quantitative and qualitative Demand has quantitative and qualitative elements, usually involves water treatmentelements, usually involves water treatment
For a given For a given cost of watercost of water, an optimal strategy can , an optimal strategy can be computed based on investment cost, be computed based on investment cost, discount rate, and project lifetime (NPV)discount rate, and project lifetime (NPV)
Water demandWater demand
intakeintake
Consumptive useConsumptive use
recyclingrecyclingreturn flowreturn flow
ProductionProductionprocessprocess
Consumptive useConsumptive use
Water demand consists of:Water demand consists of:
• Consumptive useConsumptive use– Process water (integrated in the product)Process water (integrated in the product)– Cooling (evaporation)Cooling (evaporation)
• Temporary useTemporary use (return flow) (return flow)
But pollution can make the return flow unfit But pollution can make the return flow unfit for subsequent usefor subsequent use
Conflicting useConflicting use
More than 70% of water is generally used More than 70% of water is generally used for agriculture (irrigation);for agriculture (irrigation);
Added value per unit water used in industry Added value per unit water used in industry is usually between 50 to 100 times higher is usually between 50 to 100 times higher than in agriculture;than in agriculture;
Domestic use of water is comparatively Domestic use of water is comparatively small, but with high quality requirements small, but with high quality requirements and low elasticity.and low elasticity.
Environmental use Environmental use (low flow, quality constraints).(low flow, quality constraints).
Water PollutionWater Pollution
1.1. Industrial effluents incl.spillsIndustrial effluents incl.spills
2.2. Domestic sewageDomestic sewage
3.3. Irrigation return flowIrrigation return flow
• Reduces potential utility for other down-Reduces potential utility for other down-stream usersstream users
• Endangers biological systems (fish kill)Endangers biological systems (fish kill)• May accumulate over long periods May accumulate over long periods
(chemical time bombs in sediments)(chemical time bombs in sediments)
Waste managementWaste management
Waste allocation:Waste allocation:
Utilizes the self-purification potential of Utilizes the self-purification potential of natural water bodies (BOD, natural water bodies (BOD, biodegradable substances);biodegradable substances);
But many toxics and heavy metals are But many toxics and heavy metals are persistent (long term cumulative damage, persistent (long term cumulative damage, bioaccumulation, sediments).bioaccumulation, sediments).
WP 10: Comparative AnalysisWP 10: Comparative AnalysisOBJECTIVES:OBJECTIVES:• The comparative analysis of the set of The comparative analysis of the set of
scenarios for each case/scenario.scenarios for each case/scenario. • The multi-criteria comparative analysis
and selection of a non-dominated set of (Pareto optimal) alternatives
• The identification of the most promising The identification of the most promising scenario or small set of candidate scenario or small set of candidate scenarios from each test sitescenarios from each test site
OBJECTIVES:OBJECTIVES:• The comparative analysis of the set of The comparative analysis of the set of
scenarios for each case/scenario.scenarios for each case/scenario. • The multi-criteria comparative analysis
and selection of a non-dominated set of (Pareto optimal) alternatives
• The identification of the most promising The identification of the most promising scenario or small set of candidate scenario or small set of candidate scenarios from each test sitescenarios from each test site
WP 10: Comparative AnalysisWP 10: Comparative Analysis
Scenario comparison and multi-Scenario comparison and multi-criteria analysiscriteria analysis
1.1. Baseline ScenariosBaseline Scenarios2.2. Common ScenariosCommon Scenarios3.3. Specific ScenariosSpecific ScenariosANY EXTERNAL CASES ?ANY EXTERNAL CASES ?
Scenario comparison and multi-Scenario comparison and multi-criteria analysiscriteria analysis
1.1. Baseline ScenariosBaseline Scenarios2.2. Common ScenariosCommon Scenarios3.3. Specific ScenariosSpecific ScenariosANY EXTERNAL CASES ?ANY EXTERNAL CASES ?
WP 10: Comparative AnalysisWP 10: Comparative Analysis
METHOD:METHOD: • Discrete MCDiscrete MC• Pareto set (half ordering)Pareto set (half ordering)• Reference point approachReference point approach
Set of criteria; define constraints and Set of criteria; define constraints and optimization direction for each;optimization direction for each;
Optimum solution is the one nearest to Optimum solution is the one nearest to reference point (utopia).reference point (utopia).
Reference point location scales (criteria) Reference point location scales (criteria) dimensions.dimensions.
METHOD:METHOD: • Discrete MCDiscrete MC• Pareto set (half ordering)Pareto set (half ordering)• Reference point approachReference point approach
Set of criteria; define constraints and Set of criteria; define constraints and optimization direction for each;optimization direction for each;
Optimum solution is the one nearest to Optimum solution is the one nearest to reference point (utopia).reference point (utopia).
Reference point location scales (criteria) Reference point location scales (criteria) dimensions.dimensions.
WP 10: Comparative AnalysisWP 10: Comparative Analysis
SCENARIO:SCENARIO:
INPUT:INPUT: Set of Set of assumptionsassumptionsDecision and Policy Decision and Policy variablesvariables
Exogeneous variablesExogeneous variablesOUTPUT:OUTPUT: Set of indicators Set of indicators or criteriaor criteria
SCENARIO:SCENARIO:
INPUT:INPUT: Set of Set of assumptionsassumptionsDecision and Policy Decision and Policy variablesvariables
Exogeneous variablesExogeneous variablesOUTPUT:OUTPUT: Set of indicators Set of indicators or criteriaor criteria
Decision Support MethodologyDecision Support MethodologyReference point approach:Reference point approach:
nadirnadirnadirnadir
utopiautopiautopiautopia
A1A1
A2A2
A3A3
A4A4
betterbetter
efficient efficient pointpoint
criterion 1criterion 1
crite
rion
2cr
iterio
n 2 A5A5
dominateddominated
A6A6
WP 10: Comparative AnalysisWP 10: Comparative Analysis
METHOD:METHOD:
•Modify the set of Modify the set of alternativesalternatives
•Select criteriaSelect criteria•Modify constraintsModify constraints• Introduce reference pointIntroduce reference point•Reduce dimensionalityReduce dimensionality
METHOD:METHOD:
•Modify the set of Modify the set of alternativesalternatives
•Select criteriaSelect criteria•Modify constraintsModify constraints• Introduce reference pointIntroduce reference point•Reduce dimensionalityReduce dimensionality
WP 10: Comparative AnalysisWP 10: Comparative Analysis
METHOD:METHOD: Combination of indicators by Combination of indicators by RULESRULES
or simple algorithmsor simple algorithms
Quality, Quantity Quality, Quantity STATUS STATUS
Dynamically generated combined Dynamically generated combined indicators can be used for the indicators can be used for the benchmarkingbenchmarking
METHOD:METHOD: Combination of indicators by Combination of indicators by RULESRULES
or simple algorithmsor simple algorithms
Quality, Quantity Quality, Quantity STATUS STATUS
Dynamically generated combined Dynamically generated combined indicators can be used for the indicators can be used for the benchmarkingbenchmarking
WP 10: Comparative AnalysisWP 10: Comparative Analysis
RESULT:RESULT:
•Ranking order of Ranking order of alternativesalternatives
•Dominated/Pareto subsetsDominated/Pareto subsets•Distance from reference Distance from reference
point: point: nearest = bestnearest = best
RESULT:RESULT:
•Ranking order of Ranking order of alternativesalternatives
•Dominated/Pareto subsetsDominated/Pareto subsets•Distance from reference Distance from reference
point: point: nearest = bestnearest = best
WP 10: Comparative AnalysisWP 10: Comparative Analysis
The vocabulary:The vocabulary:•List of indicatorsList of indicators•Derived indicators (rule-based)Derived indicators (rule-based)
Constraints based on:Constraints based on:– Standards (WQ, reliability, ?)Standards (WQ, reliability, ?)– Distributions (e.g., drop one SD)Distributions (e.g., drop one SD)– Preferences Preferences
The vocabulary:The vocabulary:•List of indicatorsList of indicators•Derived indicators (rule-based)Derived indicators (rule-based)
Constraints based on:Constraints based on:– Standards (WQ, reliability, ?)Standards (WQ, reliability, ?)– Distributions (e.g., drop one SD)Distributions (e.g., drop one SD)– Preferences Preferences
WP 10: Comparative AnalysisWP 10: Comparative Analysis
Inidicator definitions:Inidicator definitions:
1.1. Name, aliasName, alias
2.2. UnitUnit
3.3. Allowable range with Allowable range with symbolic labelssymbolic labels
4.4. Question/definitionQuestion/definition
5.5. Inference RulesInference Rules
Inidicator definitions:Inidicator definitions:
1.1. Name, aliasName, alias
2.2. UnitUnit
3.3. Allowable range with Allowable range with symbolic labelssymbolic labels
4.4. Question/definitionQuestion/definition
5.5. Inference RulesInference Rules
WP 10: Comparative AnalysisWP 10: Comparative AnalysisReliabilityReliabilityA: RELA: RELU: %U: %V: LowV: Low [ 0,[ 0, 10, 10, 20] 20]V: MediumV: Medium [21, 25, 40][21, 25, 40]V: HighV: High [41,[41, 50, 50, 100]100]Q: What is average reliability of Q: What is average reliability of
meeting water demands on a daily meeting water demands on a daily basis ?basis ?
ReliabilityReliabilityA: RELA: RELU: %U: %V: LowV: Low [ 0,[ 0, 10, 10, 20] 20]V: MediumV: Medium [21, 25, 40][21, 25, 40]V: HighV: High [41,[41, 50, 50, 100]100]Q: What is average reliability of Q: What is average reliability of
meeting water demands on a daily meeting water demands on a daily basis ?basis ?
WP 10: Comparative AnalysisWP 10: Comparative AnalysisRULES:RULES:
IF conditionIF conditionAND/OR conditionAND/OR condition
THEN conclusionTHEN conclusion
Condition:Condition:Descriptor Operator ValueDescriptor Operator ValueQuality == highQuality == high
RULES:RULES:IF conditionIF conditionAND/OR conditionAND/OR condition
THEN conclusionTHEN conclusion
Condition:Condition:Descriptor Operator ValueDescriptor Operator ValueQuality == highQuality == high
WP 10: Comparative AnalysisWP 10: Comparative AnalysisRULES:RULES:
IF conditionIF conditionAND/OR conditionAND/OR condition
THEN conclusionTHEN conclusionCondition:Condition:Descriptor Operator ValueDescriptor Operator ValueDensity ==, <,>,!=,……. highDensity ==, <,>,!=,……. highConclusion:Conclusion:Descriptor Assignment ValueDescriptor Assignment ValueDensity = highDensity = high
RULES:RULES:IF conditionIF condition
AND/OR conditionAND/OR conditionTHEN conclusionTHEN conclusion
Condition:Condition:Descriptor Operator ValueDescriptor Operator ValueDensity ==, <,>,!=,……. highDensity ==, <,>,!=,……. highConclusion:Conclusion:Descriptor Assignment ValueDescriptor Assignment ValueDensity = highDensity = high
WP 10: Comparative AnalysisWP 10: Comparative AnalysisRULES:RULES:
IF quantity == sufficientIF quantity == sufficientAND quality == sufficientAND quality == sufficient
THEN status = very_goodTHEN status = very_goodCondition:Condition:Descriptor Operator ValueDescriptor Operator ValueReliability ==, <,>,!=,……. Reliability ==, <,>,!=,…….
highhighConclusion:Conclusion:Descriptor Assignment ValueDescriptor Assignment Value Reliability = highReliability = high
RULES:RULES:IF quantity == sufficientIF quantity == sufficient
AND quality == sufficientAND quality == sufficientTHEN status = very_goodTHEN status = very_good
Condition:Condition:Descriptor Operator ValueDescriptor Operator ValueReliability ==, <,>,!=,……. Reliability ==, <,>,!=,…….
highhighConclusion:Conclusion:Descriptor Assignment ValueDescriptor Assignment Value Reliability = highReliability = high
WP 11: DisseminationWP 11: Dissemination• Web siteWeb site• Meeting, conferences, Meeting, conferences,
scientific and technical scientific and technical literatureliterature
• Local workshopsLocal workshops
• Web siteWeb site• Meeting, conferences, Meeting, conferences,
scientific and technical scientific and technical literatureliterature
• Local workshopsLocal workshops
WP 11: DisseminationWP 11: Dissemination
•Web site, other Web site, other material ?material ?
•Meeting, conferences, Meeting, conferences, scientific and technical scientific and technical literatureliterature
•Local dissemination Local dissemination workshops (language)workshops (language)
•SMART: the book ?SMART: the book ?
•Web site, other Web site, other material ?material ?
•Meeting, conferences, Meeting, conferences, scientific and technical scientific and technical literatureliterature
•Local dissemination Local dissemination workshops (language)workshops (language)
•SMART: the book ?SMART: the book ?