VJNIT Dec2013 Compress

8/13/2019 VJNIT Dec2013 Compress

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Role of Technology in Disaster Management

V [email protected]

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
http://www.gpsreview.net/wp-content/uploads/lockheed-martin-gps-satellite.jpg


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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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.

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01020

3040506070

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5 J u n

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1 9 J u n

2 6 J u n

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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

Documents

VJNIT Dec2013 Compress