DISASTER RISK REDUCTION FOR SUSTAINABLE DEVELOPMENT: MAKING INDIA RESILIENT BY 2030

NPDRR SECOND MEETING

ON 15 & 16.05.2017 AT VIGYAN BHAWAN, NEW DELHI

1

Presentation by

Dr. Korlapati Satyagopal., I.A.S.Principal Secretary / Commissioner of Revenue Administration & State Relief Commissioner

Tamil Nadu

NATIONAL DISASTER DATABASE - NEED AND CHALLENGES

Principles of Emergency management

1. Comprehensive

2. Risk-driven

4. Collaborative

5. Coordinated

6. Creative and innovative

7. Science and knowledge-based approach for continuous

improvement.

Disaster Database has to address all the above principles

Planning & Policy decision

1. Legacy Data for trend & pattern

analysis.

2. Hazard mapping & Vulnerability

Assessment.

3. Database of disaster management plan.

4. Awareness & training materials.

5. Inventory of legal, techno legal,

administrative & institutional

framework.

6. Database of Financial sources.

Disaster Database for Emergency Management

Quick emergency Response &

Recovery

1. Human & Material response

resources database.

2. Database of Infrastructure,

lifelines & critical facilities.

3. Database of trained human

resources.

4. Demographic information.

5. GIS based information

system and simulation

modelling.

Disaster Database is relevant in order to address all the 4

priorities under Sendai Frame work for Disaster Risk Reduction;

Priority 1: Understanding disaster risk.

Priority 2: Strengthening disaster risk governance

Priority 3: Investing in disaster risk reduction for resilience.

Priority 4: Enhancing disaster preparedness for effective response, and to «Build Back Better» in recovery, rehabilitation and reconstruction.

Disaster Database - NDEM

• National Database for Emergency Management (NDEM)

is conceived as a GIS based repository of data to support

disaster / emergency management in the country.

• NDEM is planned as a multi-institutional coordinated

effort & encompasses all emergency situations arising out

of disasters.

• It assists the disaster managers at various levels in

decision making for managing emergency situations.

Accomplishments

Existing Disaster Database - NDEM

• Dashboard

• NDEM provides disaster related live & historical

news/alerts/warnings obtained from the available sources.

• Daily rainfall, temperature, water level etc., are

integrated into dashboard as a service.

• It is proposed to link Data forecasting agencies such as

IMD, CWC, INCOIS etc., with NDEM portal for directly

obtaining the daily updates through dashboard.

Existing Disaster Data Organisation - NDEM

BASE LAYERS

1 State

2 District

3 Taluk

4 Village Boundaries

5 Road

6 Rail

7 Drainage

8 Canal

9 Coastline

10 River

THEMATIC LAYERS

11 Land use / land cover

12 Settlement-area

13 Mining Area

14 Surface water bodies

15 Forest Boundaries

16 Settlement-Point

17 Slope

18 Meteorological data (Point)

Core Data

Existing Disaster Data Classification - NDEM

INFRASTRUCTURE

19 Railway stations

20 Hospitals

21 Airports

22 Helipads

23 Ports

24 River Gauge Stations

25 Ponds & Tanks

26 Dams(Point)

27 Dams(Area)or Reservoir

28 Power plants

29 Point of Interest

RASTER

(Satellite data Products)

30 LISS IV MX

31 Carto2 DEM

32 ACE2 DEM

33 SRTM DEM

Disaster Specific

Data34 Flood

35 Cyclone / Tsunami

36 Forest Fire

37 Earthquake

38 Landslide

39 Drought

Non- Spatial

Database

40 Socio Economic

41 Census 2011

42 IDRN 2014

43 Health Data

Existing Disaster Data Classification - NDEM

Cyclone and Monsoon Rainfall Forecast System(from 15 days to current for Rainfall, Cyclone Genesis, Intensity and track forecasts)

SDSC

SHAR

ISRO

- ISRO (WRF & SATOBS)

- ECMWF EPS & DET

- NCEP GFS

- UK MET

- NOAA

- JTWC

- EUMETSAT & Himawari

SATOBS

- IMD DWR & MBLM

Ensembles

Deterministic &

Probabilistic

forecast

SDSC (Satish Dawan Space Centre )

SHAR (Sriharikota)

ISRO (Indian Space Research Organisation)

Developed for Andhra Pradesh based on MOU

• IDRN is a nation-wide electronic inventory of

resources that enlists equipment and human

resources, collated from districts, states and

national level agencies.

• At present IDRN has about 1.5 lakhs records

from all the states / UT’s of the country.

India Disaster Resource Network (IDRN)

TNSDMA

• Vulnerability analysis at Firka level (sub-taluk) in

Rural areas and Ward level in Urban areas.

• Primary data is converted into Maps.

Existing Spatial Data available in Tamil Nadu

• Administrative boundaries

• Health and Hospitals

• Transport network

• Schools

• Water resources

• Watersheds

• Tele communications

• Dams and Reservoirs

• Forests

• Public infrastructures

• Sports & Stadium

• Industrial locations

• TNEB

• Police stations

• Database of Bridges and

Culverts.However, integration of GIS Layers for

emergency management is under process.

• Integration of legacy data of vulnerability with forecast

information from IMD, INCOIS, CWC, etc.,

• Mobile based applications for gathering Big data

both from Government sources (multiple agencies) &

Crowd sourcing of disaster related data

through Social Media platforms.

Challenges

•Areas in harm's way to be identified real time, to

provide warnings and notifications to the

stakeholders

of pending, existing, or unfolding emergencies

based on the location or areas to be impacted

by the incident – Push SMS.

• Validation and regular updation of data.

Challenges

Development of simulation models by

linking legacy data, rainfall data(Big data) &

forecast data

using Predictive Analytics & other Data

Analytics to

assess the inundation levels and

vulnerability of different locations (including

data from crowd sourcing)

during Storm Surge/or any disaster.

Challenges

Challenges

Maps of Chennai Floods 2015:Flood inundation in Chennai City:

Currently doesn’t have

• Depth.

• Period of inundation.

• Vulnerable population

details.

• Source analysis.

•Link to details of First

responders.

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Kavanur

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Ramapuram Saidapet

Mambalam

Perambur

Kadaperi

Nolambur

Ariyalur

Agaramel

Korathur

Pandeswaran Koduveli

Kummamur

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Athivakkam Vadagarai

mbedu

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Kalpakkam

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heri

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Madippakkam Palavakkam

Medavakkam

Ayappakkam

Soorapattu

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Manapakkam

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Pudukuppam Sholavaram

Sekkanjeri

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Mannivakkam Kailancheri

akkam

Perumbakkam

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Alamathi RF

Puludivakkam

Tiruvanmiyur

Pudvannarpet

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Arakambakkam

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Nandambakkam

Voyalanallur

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Solinganallur

Adayalampattu

aiyur

Pidarithangal

Kolappancheri

Thiruninravur

Edayanchavadi

Panaiyurkuppam

Sikkarayapuram

Koilpadagai RF

Poonamallee

Jaganathapuram

Fort St. George

Ponneri

Thiruvallur

Sriperumbudur

Chengalpattu

Uthukkottai

Chennai

Alandur

Gummidipoondi

Kancheepuram

Uthiramerur

Source: RISAT,Dec,2015, NRSC, Prepared by GIS Cell: Tamil Nadu State Disaster Management Agency, Revenue Administration

Tiruttani

Tiruttani

Thiruvallur

Kancheepuram

Chennai

Cholavaram Lake

Redhills (Puzal) Lake

Poondi Reservoir

Chembarambakkam Tank

Sriperumpudur Tank

Scale

0 2.5 5 10 km

±Chennai Metropolitan Boundary

LEGENDFlood inundation

T A M I L N A D U S T A T EN o r t h e a s t M o n s o o n - 2 0 1 5

C H E N N A I M E T R O P O L I T A N

F L O O D I N U N D A T I O N A R E A S

5, 6, 7 December, 2015

4, December, 2015

3, December, 2015

Reservoirs

River

Tanks

Taluk Boundary

District Boundary

r

B a Y

0 f

e n g a 1

Tenneri Tank

Towards Arakkonam

!. Major Locations

District Road

National Highways

Railway line

Categorisation of Inundated areas

An attempt was made to categorise the inundated areas

based on dates on which the areas were inundated.

duration of inundation.

• Capturing geo-tagged images of Vulnerable areas

based on

• historical data,

• during and post disaster phases,

• so that field data & high resolution satellite images

can be integrated for emergency management.

Recommendations

In every State

• Areas of Very high Vulnerability & High Vulnerability

should be mapped for different disasters

using Aerial photogrammetry(UAV based),

Light Detection and Ranging (LiDAR) &

High Resolution Satellite Imagery

for use on a GIS Platform.

• GIS applications should be developed for efficient

emergency management.

Recommendations

Need to develop 3D/2D simulation models forStorm Surge, Flood inundation, Seismicity to

a) Predict areas in the harm’s way

b) Intensity of the hazard

c) Assess assistance required for rescue &

evacuation

d) Initiate arrangements for relief operations

e) Assist in Prevention, Preparation and Mitigation

measures

Recommendations

• To meet the emergency management needs,

professional tools and technology such as Data

Analytics (Descriptive analytics, Predictive analytics

and Prescriptive analytics) are required.

• However, the technology has to be robust to withstand

attacks of Virus (WannaCry Ransomware episode)

Conclusion

Technology is very important

but

.Institutional and

Organisational

strength

are more

important

THANK YOU

• Big Data analytics have moved from being descriptive (based on past information using statistics – Business Intelligence to understand what happened) to inquisitive analytics (why it happened) to being predictive (used past information to predict future outcomes- Data mining and forecasting for what is likely to happen) to being prescriptive (used past information to direct future results –optimization to arrive what should happen).

• Big data analytics is the process of examining large and varied data sets --i.e., big data -- to uncover hidden patterns, unknown correlations,, and other useful information that can help organizations make more-informed decisions.

• Large volumes of data sets — commonly referred to as big data — derived from sophisticated sensors and social media feeds are increasingly being used by government agencies to improve citizen services through GIS mapping.

• Predictive analytics is the branch of the advanced analytics which is used to make predictions about unknown future events.

• Predictive analytics uses many techniques from data mining, statistics, modeling, machine learning, and artificial intelligence to analyze current data to make predictions about future.

Big Data is the name given to our ever-increasing ability to collect more data from a multitude of sources, and analyze it for insights using advanced computer algorithms.

In data analytics are classified into three levels according to the depth of analysis:

• Descriptive Analytics : exploits historical data to describe what occurred.

• Predictive analytics: focuses on predicting future Probabilities and trends.

• Prescriptive analytics: addresses decision making and efficiency.

For example, simulation is used to analyze complex systems to

gain insight into system behavior and identify issues and

optimization techniques are used to find optimal solutions

under given constraints.