Role of AI in Disaster Management

Gist During recent natural disasters in Nepal, authorities and volunteers successfully deployed AI-powered web portals and thermal-equipped drones to trace missing persons and map structural damage. This marks a fundamental shift in disaster management, moving away from purely physical, state-led responses toward decentralized, technology-driven rescue models utilizing crowdsourced data. For UPSC aspirants, understanding this integration of Artificial Intelligence into early warning systems, rescue operations, and post-disaster recovery is crucial for evaluating modern disaster resilience strategies. Background Traditionally, disaster management has relied heavily on centralized state authorities and conventional weather forecasting. These legacy forecasting models use complex physics equations that demand immense computing resources and time, making it exceedingly difficult to generate accurate, hyperlocal alerts during fast-moving crises. Furthermore, post-disaster search and rescue operations historically depended on manual visual surveys and limited physical assets like helicopters. The integration of Artificial Intelligence AI fundamentally alters this mechanism: instead of calculating physics equations from scratch, machine learning models are trained on vast historical datasets to "learn" atmospheric behavior, allowing them to predict weather patterns and process massive volumes of unstructured field data in…