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Early warning, emergency response, and disaster resilience systems
6 Problem Statements in this Domain (5 Standard + 1 Open Innovation)
Glacial Lake Outburst Floods (GLOFs) can devastate downstream Himalayan settlements with almost no warning, and while satellite monitoring can track lake area growth over time, it cannot capture the rapid precursor dynamics (ice-dam weakening, sudden inflow surges) that precede an actual breach — and India has very few in-situ sensors in these extremely remote, high-altitude, hard-to-access lake sites. The problem requires generating a useful early warning from a combination of infrequent satellite observation and whatever minimal, intermittently-reporting in-situ sensing is feasible to deploy and maintain in such an inaccessible environment.
In landslide-prone hill regions, victims can be buried under loose debris with survival windows measured in hours, but current search relies mainly on trained dog teams and manual probing, which do not scale to large debris fields and can be slowed by unstable, still-moving ground that is unsafe for both rescuers and dogs. Locating a live victim under several meters of loose, heterogeneous debris — distinguishing faint life signs (movement, sound, breathing) from ambient debris settling noise — is a genuinely difficult signal-detection problem, not a simple sensor placement task.
After a significant earthquake, thousands of buildings may need rapid structural safety assessment to decide which are safe to re-enter, which need evacuation, and which are at collapse risk — but India has far too few structural engineers to physically inspect every affected building within the critical early days, and formal engineering assessment protocols were not designed for this kind of triage-speed, large-scale application. A workable rapid-triage method must extract meaningful structural risk signals from readily obtainable data (drone imagery, ground photos) despite highly variable, non-engineered building typologies typical of Indian urban construction.
Cyclones frequently trigger cascading power outages — an initial localized failure (fallen line, damaged substation) can trigger protective trips and load redistribution that propagate failures well beyond the initially damaged area, complicating both restoration planning and coordination with emergency shelters/hospitals that need power prioritized. Predicting which specific parts of a grid are at cascading failure risk, ahead of and during a cyclone, requires combining structural grid vulnerability data with real-time weather trajectory data — a problem current utilities generally do not model at this level of integration.
Major disasters routinely damage or overload communication infrastructure exactly when multiple agencies (police, NDRF, medical teams, NGOs, local administration) most need to coordinate resource allocation, avoid duplicated effort, and share situational information — but agencies currently rely on ad-hoc phone calls and radio, with no shared, resilient information layer that functions when connectivity is fragmented, intermittent, or entirely local (mesh-only). The challenge is maintaining a coherent, reasonably up-to-date shared operational picture across agencies despite each having only intermittent and inconsistent connectivity to each other and to any central system.
Have your own innovation in Disaster Management? Design your own technological solution for flood/landslide early warning, search-and-rescue robotics, offline emergency mesh communication networks, disaster logistics, or crowdsourced relief resource routing.