DISASTER RISK REDUCTION FOR SUSTAINABLE DEVELOPMENT: MAKING RESILIENT BY 2030

NPDRR SECOND MEETING

ON 15 & 16.05.2017 AT VIGYAN BHAWAN, NEW DELHI

NATIONAL DISASTER DATABASE - NEED AND CHALLENGES

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

Tamil Nadu 1 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 Disaster Database for Emergency Management

Planning & Policy decision Quick emergency Response & Recovery

1. Legacy Data for trend & pattern 1. Human & Material response analysis. resources database. 2. Hazard mapping & Vulnerability 2. Database of Infrastructure, Assessment. lifelines & critical facilities. 3. Database of disaster management plan. 3. Database of trained human 4. Awareness & training materials. resources. 5. Inventory of legal, techno legal, 4. Demographic information. administrative & institutional framework. 5. GIS based information 6. Database of Financial sources. 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. Accomplishments 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. 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 Core Data BASE LAYERS THEMATIC LAYERS 1 State 11 Land use / land cover 2 District 12 Settlement-area 3 Taluk 4 Village Boundaries 13 Mining Area 5 Road 14 Surface water bodies 6 Rail 15 Forest Boundaries 7 Drainage 16 Settlement-Point 8 Canal 9 Coastline 17 Slope 10 River 18 Meteorological data (Point) Existing Disaster Data Classification - NDEM INFRASTRUCTURE RASTER 19 Railway stations (Satellite data Products) 20 Hospitals 21 Airports 30 LISS IV MX 22 Helipads 23 Ports 31 Carto2 DEM 24 River Gauge Stations 25 Ponds & Tanks 32 ACE2 DEM 26 Dams(Point) 33 27 Dams(Area)or Reservoir SRTM DEM 28 Power plants 29 Point of Interest Existing Disaster Data Classification - NDEM Disaster Specific Non- Spatial Data Database 34 Flood 40 Socio Economic 35 Cyclone / Tsunami 41 Census 2011 36 Forest Fire 37 Earthquake 42 IDRN 2014 38 Landslide 43 Health Data 39 Drought Cyclone and Monsoon Rainfall Forecast System (from 15 days to current for Rainfall, Cyclone Genesis, Intensity and track forecasts)

- ISRO (WRF & SATOBS) - ECMWF EPS & DET - NCEP GFS Ensembles - UK MET SDSC Deterministic & - NOAA SHAR - JTWC Probabilistic - EUMETSAT & Himawari ISRO SATOBS forecast - IMD DWR & MBLM

Developed for Andhra Pradesh based on MOU SDSC (Satish Dawan Space Centre ) SHAR (Sriharikota) ISRO (Indian Space Research Organisation) India Disaster Resource Network (IDRN)

• 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. 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 • Administrative boundaries • Forests • Health and Hospitals • Public infrastructures • Transport network • Sports & Stadium • Schools • Industrial locations • Water resources • TNEB • Watersheds • Police stations • Tele communications • Database of Bridges and • Dams and Reservoirs Culverts. However, integration of GIS Layers for emergency management is under process. Challenges

• 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 Maps of 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. T A MIL N A D U S T A T E N o r t h e ast M o n s o o n - 2 0 1 5 Scale 0 2.5 5 10 km CHEN NAI M E T ROPO LIT A N FLO O D INU N D A TIO N AREA S Categorisation of Inundated areas LEGEND Flood inundation Reservoirs !. Major Locations 5, 6, 7 December, 2015 ± River District Road 4, December, 2015 Tanks National Highways 3, December, 2015 Taluk Boundary Railway line District Boundary Chennai Metropolitan Boundary Kalpakkam !. Uthukkottai Vannipakkam An attempt was made to !. M!.injur !. Jaganathapuram !. Kollatti !. Sekkanjeri Madiyur !. !. !. Nayur !. !. !. !. !. Vallur Kottakuppam Athur (I)!. !. Orakkadu !. Seemapuram!. !. !. !. !. !. .! .! Chinnamullavoyal New Erumaivettipalayam Karamodai Bud!.ur !. !. !. !. Pudukuppam Sholavaram Angadu!. !. Arum!. andai Vellivoyal categorise the inundated areas .! !. !. !. Lake .! !. !. Singilikuppam Nallur !. Siruniam Marambedu !. !. !. !. !. .! !. Alamathi Kummamur !. Karanai !. Sirugavur Thiruvallur !. !. Vij!.ayanallur !. Pandeswaran Koduveli !. !. Lyon Ariyal!. ur !. Alamathi RF !. !. !. Arakambakkam !.Vada!.garai !. Kilakond !. !. Athivakkam Kosapur aiyur !. Kad!.avur !. !. !. !. !. !. Morai !. !. Thandalkalani !. Vellacheri !.  based on dates on which the Alathur Pothur Vilakkupattu !. !. !. !. Palavedu Redhills (Puzal) Lake !. Soorapattu !. !. Pudvannarpet Thandarai RF !. !. !. Koilpadagai RF Kulathur Towards Thiruvallur .! .! areas were inundated. !. Agraharamel Basin B!.ridge Tiruttani !. Amudurmedu !. !. George Town !. !. !. !. !. Tiruttani Voyalanallur !. Kilpakkam Central Korathur .! !. !. !. Kolappancheri !. !. !. Fort St. George Neman !. Parivakkam !. !. !. Kuthambakkam!. Vellavedu !. !. Adayalampattu Pidarithangal !. !. !. Parvatharajapuram!. Narasingapuram !. !.  Chennai duration of inundation. !. Agaramel !. !. !. !. Ramapuram Mylapor!.e !. Mang!.adu Meppur !. !. !. !. !. !. !. Paraniputhur Mowlivakkam Sikkarayapuram !. !. !. !. !. Chembarambakkam Tank !. Ki!.vur !. Kol!.apakkam!. !. !. Chennai Kollacheri Thandalam !. !. !. Kavanur Tharapakkam Tharavur !. B a Y !. !. !. Tiruvanmiyur !. !. !. !. Velachcheri !. Puludivakkam !. !. !. Madippakkam Sriperumpudur Tank Poonthandalam !. !. !. !. !. !. Erum!. aiyur !. Koilambakkam Neelamkarai !. !. !. !. Naduveerapattu Erumaiyur RF!. !. Nanmangala!.m !.

Sriperumbudur !. Kar !. akkam 0 f Mudichchur Jalladiam!.pet ap Varadha!.rajapuram!. !. Madamb akkam Solinganallur !. !. !. !. P!.uthur Panaiyurkuppam Pad!.appa!.i Kasbapur!.am !. Agaramthen !. !. !. !. !. !. !. Kailancheri Kolapak!.kam !. !. Arasankalani !. Moolac!. Uthandi Tenneri Tank Urappakkam heri !. !. !. !. !. Ponmar Semmanjeri Kancheepuram !. Unamancheri !. !. !. !. B Unamancheri RF Navalar Gudvancheri !. !. e n g a 1 !. Thazhambur Sirucheri !.

Kancheepuram

Chengalpattu

Uthiramerur

Source: RISAT,Dec,2015, NRSC, Prepared by GIS Cell: Tamil Nadu State Disaster Management Agency, Revenue Administration Recommendations • 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 for Storm 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 Conclusion • 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) 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.