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Role of Technology in Disaster Management
Workshop on Disaster Preparedness, Management and Risk Reduction
December 09, 2013 under Centre of Excellence in Disaster MitigationCivil Engg Dept, VNRVJIET, Hyderabad
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Can We Stop Disasters No, Mostly
Can We Control Disasters Not Totally
Can We Predict Disasters Not Many
Can We Manage Disasters Partly Yes
What We Can do ???
Understanding Disasters
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DISASTERS
NATURAL DISASTER1. Floods (CWC)
2. Cyclones (IMD)3. Forest fires (MoEF)4. Landslides (GSI)5. Drought (MoA)6. Spread of epidemics (MoH)7. Earthquakes (IMD)
8. Tsunami (MEoS)9. Severe windstorms (IMD)10.Snow or ice storms (IMD)11.Extremes weather Events( IMD)
TECHNOLOGICAL DISASTER / MAN-MADE1. Fire (State Govt)
2. Explosion ((State Govt, MHA)3. Building collapse (State Govt)4. Train/ vehicle accidents (IR, State Govt)5. Failure of dams (CWC)6. Major structural failure (State Govt)7. Spills of flammable liquids (MoEF)
8. Accidental release of toxic substances (MoEF)9. Chemical Disasters (MoEF)10.Mining accidents (MoM)11.Loss of electrical power (State Govt)12.Loss of water supply (State Govt)13.Loss of communications (State Govt, DoT)
14.Nuclear / Radiological disaster (DoAE)Early Warning available (3)
Type of EmergencyEvacuation, Rescue&Relief
Medical,Contingency Operations
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Evacuation: Safe guarding against impact Means of Transport, availability, applicability ( Air / land / water)Shelter identification, optimal path (path restrictions, traffic density)
Relief Mgmt: Taking care of victims Air Dropping of essential items, essential needs at shelter (sanitation,lighting etc), survival needs (food, water, medicines etc), reliefdistribution, clearing debris / ways/ bodies), restoration etc
Medical: Life supportLimited Causality Means of Transport, availability, applicability ( Air / land / water)
Selection of health services ( hospital / diagnostics etc)
Mass Causality Availability & Applicability of mobile health services, Means ofTransport, availability, applicability ( Air / land / water) Selection of
health services ( hospital / diagnostics etc), categorization of patients,informing public about status of activities etc
Contingency : To limit / minimize the extent of impact Operations Event Specific to the event, follow the individual Ministries SoPs for
emergency management
Type of Emergency Plans Required
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EventReporting
Real-timeField Info
Situation Assessment
ResourceIdentification
Mobilization
Nodal Depts+
CommnChannels
+
Database integration, VisualizationDecision Making, Actions
Core DatabaseFacilities databaseDynamic databaseThematic Database
Disaster/Emergency Management Concept
Decision Support System
1. Built-in core & Static data2. Live data reception3. Scenario visualization4. Probable options to deal5. Choosing best option6. Means to implement
7. Broadcasting capability
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Disaster Management System
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Con vergence of Geos pat ial Tech no log ies
Total TurnkeySolutions
ALTM
Bandwidth
GPS
RSGIS
ICT
LBS
Photo-grammetry
TotalStation
Convergence
...cu t t in g edg e techn olo gies
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Mobile Technology
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Mobile based Application forCollection of Field Data on Emergency Facilities
Real time data collection on GPScoordinates, Digital Photos & userspecified parameters andtransmission from field to centralprocessing server
Demonstrated on pilot scale inPuri Dist, Orissa in collaborationwith OSDMA for collecting Info onRelief Shelters/ Hospitals/ CivilGodowns.
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Mobile based ApplicationParameters collected
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Mobile PDA ( built-in GPS & Camera)OS Windows Mobile 6.1 ProfessionalGPRS ConnectivityStorage /Processing Servers
Relief Management
Visualization of calls on Spatial Map Viewer
Visualization of calls on ISRO-Bhuvan Portal
A mobile based application, for real time field datacollection through PDA device, transmission throughGPRS, reception at central server for processing,analysis and visulisation and communication back to
the field PDA and concerned departments for effectiverelief management.
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Disaster Event Reporting System Prototype
Features
Ordinary GSMmobile
SMS based
Registeredmobiles withgeo-location
Mobile Server
Storage/Processing Servers
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Mobile Application
for Accident Reporting
Short Reporting
Detail Reporting
Railway Accident Management
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S Ob i
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High temporal resolutionLarge swath
Medium temporal resolutionLarge swath
Low temporal resolutionLimited swath
Low temporal resolutionVery limited swath
High spatial resolutionLocation specific information
Coarse spatial resolutionRegional level information
Medium spatial resolutionLocal level information
Space ObservationsDepends on Phase of the disaster Type of the disaster Extent and severity.
Low spatial resolutionGlobal level information
Global to Local
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Hand-held/ MobileMessaging Terminal
C W D S /
D C W D
S
Satellite (INSAT) basedEmergency Communication Systems
S a t e l
l i t e
P h o n e
D T H b a s e d
D W D S
F i s h e r m e n
D A T
Demonstrated to all concerned Put/ being put into operational
use
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KedarnathTemple
CARTOSAT-2A image of 20-June-2013 showing
post-flood situation
CARTOSAT-2A satellite image of 20-June -2013 showingdamages in Kedarnath and downstream
Road cut-off
Road cut-off
Debris
Debris
Uttarakhand Floods
K d th T l di D t
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KedarnathTemple
Description Count
Structures Intact 66
Damaged 47
Washed away 63
Retaining wall(washed away)
1
Total 177
Kedarnath Temple surroundings Damage assessment
Data used:Pre-Floods: Cartosat-1 (year 2011)Post-Flood: Cartosat-2 (20 th Jun 13)
U kh d Fl d
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CARTOSAT-1 image showing pre-flood
situation
CARTOSAT-2A image of 20-June-2013 showingpost-flood situation
Road cut-off
Road
Debris
Boulders
CARTOSAT-2A satellite image of 20-June -2013showing damages in Kedarnath and downstream
n
Uttarakhand Floods
CARTO & LISS-IV merged product of 20 th & 21 st June-2013
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Kedarnath
n
g phighlighting the stream accumulations into River Mandakini
Flood Affected Area
Landslide Inventory Mapping
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Entire Mandakini valley and parts of Alaknanda valleywere mapped.
Major towns covered are Kedarnath, Gaurikund,Sonprayag, Okhimath, Agastmuni, Rudraprayag andGauchar
Total no. of landslides : 1163 ( note: not field validated )
Landslide inventory maps are updated in Bhuvan
Summary of landslide inventory
Landslide Inventory Mapping
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nrsc
Kharida
Hinjilikatu
Jhadabandha
JagadisPahad
Jayapur
Sialia
Jamunagiri
Asika Ambapua
Kabisuryanagar
Karnoli
KharidaHinjilikatu
Jhadabandha
JagadisPahad
Jayapur
Sialia
Jamunagiri
Asika Ambapua
Kabisuryanagar
Karnoli
VijayawadaRanchi Express
Highway VijayawadaRanchi Express
HighwayFLOODED
UK-DMC2 data of Jan 25,2013 UK-DMC2 data of Oct 14,2013Pre-Cyclone Post-Cyclone
Flood Inundation
CYCLONE PHAILIN-2013, ODISHA
U F i dl P d t
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User Friendly- Products
Provides thespatial extent of
flood inundation
Format Jpg, Pdf Geo-Pdf
State-Level Flood Map
Providessubmerged
roads, rails
With villageboundary overlay
Detailed Flood Map
District-Level Flood Map
Helps infurther valueaddition atUser end
Flood Layer in GIS format
Assam: Flood Ha ard Zonation
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Assam: Flood Hazard ZonationState-Level
Prepared based on 94 satellite data setsacquired during different magnitudes offloods during 1998 - 2007
Flood In ndation Sim lation Part of Goda ari Ri er Basin
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Flood Inundation Simulation Part of Godavari River Basin
Flood Inundation(Observed)
Flood Inundation(Simulated)
Flood Inundation(Validation)
August 07, 2006 August 07, 2006
07-Aug-06 - 72,214 ha06-Aug-06 - 85,386 ha 82,497 hectares
Results Flood Depth Map
P f N Di i A Fl d H d
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Part of Nowgomg District Assam: Flood Hazard
331 Villages under Very High &High Hazard categories
N TION L GRICULTUR L DROUGHT SSESSMENT ND MONITORING SYSTEM
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.
-100
-50
0
50
100
150
200
250
300
.
12/6 19/6 6/6 3/710/7 17/7 24/7 31/7 7/8 14/8 21/8 28/8 4/9 1 1/9 18/9 25/9
% d
e v i a
t i o n
.
01020
3040506070
8090
100
5 J u n
1 2 J u n
1 9 J u n
2 6 J u n
3 J u l
1 0 J u l
1 7 J u l
2 4 J u l
3 1 J u l
7 A u g
1 4 A u g
2 1 A u g
3 1 A u g
1 1 S e p
1 8 S e p
2 5 S e p
3 0 S e p
% o
f n o r m a
l
N TION L GRICULTUR L DROUGHT SSESSMENT ND MONITORING SYSTEM
Coverage Satel l i te data analysis
In tegra t ion wi th g round da ta
Informat ion report ing
Drought assessment
AWiFS
AVHRR
Rainfall deviations Sowing progress
AWiFS MODIS 250 mts MODIS 1 km AVHRR
Indicators/informationbeing used indrought assessment
NDVI NDWI EVI AMSR E soil moisture CPC rainfall forecast Soil Rainfall Sown area Cropping pattern Irrigation support
Agricultural area NDVI of NOAA AVHRR (1 km * 1km) kharif 2011
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Agricultural area NDVI of NOAA AVHRR (1 km * 1km), kharif 2011
AugustJulyJune
September OctoberNovember
Daily Active Forest Fire Alerts During Feb
-
June Every Year
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Daily Active Forest Fire Alerts During Feb June Every Yearbased on TERRA/AQUA MODIS Data
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Daily active forest fires over india during April & May 2012
APRIL 2012 MAY 2012
MonthNo. of FireDetections
Feb-2012 9152
Mar-2012 25322
Apr-2012 4883
May-2012 507Total 39864
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GIS & Web Technology
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14Oct 2013 Flood Layer showing submerged taluks of Balasur & Gunjam
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14Oct 2013 Flood Layer showing submerged taluks of Balasur & Gunjam
Detailed Map view of Balasur Dist.
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pShowing submerged Rail & villages using 14oct Radarsat 2 datasets
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D i i S C (DSC) S i
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Flood Inundation Maps Damage Assessment Hazard Zonation Bank Erosion Studies
Floods
MonthlyAgril.Drought Report
End-of-the-SeasonAgril. Drought Report
Drought Inundation Maps Recession Maps Damage
Assessment
Cyclone
DamageAssessment
Earthquake
DamageAssessment
Hazard Zonation
Landslide Active Fire
Detection Damage
Assessment
Forest Fire
Information DisseminationCentral: MHA, CWC, Min. of Agri, GSI, IMD, MOEF
State: Relief Commr., DM, Agri, Forest, other concerned Line Depts.
Decision Support Centre (DSC) ServicesSeasonal Monitoring Event Based Monitoring
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Challenges
Some Challenges
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Some Challenges
DomainKnowledge
Generation
OfInfo
EndUtilization
Use FriendlyInterface among Depts
Capacity Building...
Earthquake PredictionLandslide Forecasting
...
Database Development
Real Time Data collection &Processing
Development of Models
Computational Speed
Tools in Distributed Environment
Connectivity
Emergency Communication
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Changing Emphases: from Data to Analysis
Data Conversion
Attribute Tagging
Spatial Analysis Data Conversion
Spatial Analysis
Attribute Tagging
Present Future
h ll
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Image Processing Challenges
Pattern Reorganization Automatic feature extraction, Artificial Neural Networks,Parallel Processing .. .
Content / Object based info retrievalParameterization, statistical distributions,
knowledge based classification
Object detection
Artificial Intelligence & Robotics
Future Needs
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Petabytes of storages and beyond From Remote Sensing Satellites, & Global Scientific missionneeds tremendous storage
High speed Networks (Terabits per second)Key enabler to support data and bandwidth intensive applications
Grid Computing, Problem solving environments on
web and collaboration tools High Performance Computing - Petaflop and beyond
Secure Cyber InfrastructureDemand for trusted and reliable infrastructure service isincreasing .
Sensor WebSensor technology, computer technology and network technologyneed to advance together to connect information systems with thereal world
Future Needs
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Conclusion
Right TIP to DM
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P eople
Rainfall
IMD, CWC
A few days in advance
Occurrence of a flood wave
CWC
At least 24 hours in advance
Forecast & Warning
State Relief Commissioner
At least 24 hours in advance
Likely disaster Impact
People in the likely affected area
At least few hours in advance
Inundation Extent/Damage caused
State Relief Commissioner
As early as possible
Hazard & vulnerability After the event
Flood control De artment
Relief / rescueImmediately
Victims
TIP
g
Ri ht TIP t Di t M t
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Right Information
Right P erson
Right TimeMinutes
ToDays
Simple Text Msg
ToLarge spatial Info
Top AdministratorTo
Common Man
Right TIP to Disaster Management
What is Right Time What is Right Information Who is the Right Person Are we in a position to generate Right Info Are we in a position to communicate toRight Person at Right Time
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Talking about Disaster Management is
EASY
Conceiving & Implementation is a
CHALLENGE