3. The Problem Statement
• Natural disasters are caused by
– Floods resulting in traffic disruption, loss of crops,
people, animals etc
– Cyclone devastation in coastal areas
– Earthquake, Landslides, tsunami & Avalanches
• Delayed warning of natural disasters causing
extensive loss/damage
• Absence of relief infrastructure
4. The Solution – Flood as pilot
• Objective is to identify hot spots based on past
incidents, predict future disasters, aid relief measures,
provide reports for real time analytics in a cost
effective manner with an intent to enable trauma care
with the help of businesses that focuses on trauma
relief
– Early Warning - Effective geo-spatial communication
system & use of analytics and predictive algorithms
– Infra setup - Provide proper means of uninterrupted
infrastructure with redundancy/continuity & Faster and
alternate data connectivity via cloud for cost effectiveness
– Actual relief - Convergence of agencies driven by dedicated
toll-free services for trauma care
5. The Constraints
• Availability of weather and met data on real-
time basis
• Investments related to setting up redundant
infrastructure
• Power during and post disaster
• Interest/common goal by other agencies
6. Implementation Steps
• Set-up sensors and M2M platform for flood prone areas
• Tie up with Met/Weather office for satellite data
• Usage of big data to arrive at predictive analytics
algorithm
• Identify Hot-spots, send early warning and invoke
disaster mitigation plans using Push Notification from
cell tower
• Integrated portal for intra-agency coordination for relief
measures
• Set-up toll free services and PRI
• Monitor, track and coordinate relief
• Communication of outcome to all stakeholders
7. Technologies Used
• Sensors
• M2M, Push Notification from Cell tower
• Toll free services and PRI
• Solr open source enterprise search platform
• Advanced Big data analysis with Cloudera
Hadoop and R
8. Outcomes
• Accelerated pace of warning
• Reduced damage/loss
• Proactive and timely evacuation
• Effective trauma care
• Enhanced prediction at low cost and <80% loss
• Delivers a flexible solution topology using ‘power of many’
• Improvement of forecasting, warning, communication and
effective pro-active disaster management
• 75% improvement in disaster management than last
year
