UPSC Mains Answer Practice — GS 3: role of AI in transforming disaster management and Vulnerability of subsea cables (Week 174)

Disaster management in India can be revolutionized by Artificial Intelligence (AI), which rapidly processes vast amounts of data and creates localized forecasts and risk assessments. AI is shifting disaster management from a reactive stance to a predictive, targeted approach. Predictive AI models can swiftly analyze extensive weather data, both historical and real-time, to provide more precise and localized weather forecasts.

These AI models complement traditional physics-based weather forecasting techniques, with the India Meteorological Department (IMD) currently piloting models like Pangu, GraphCast, and FourCastNet. In 2026, the IMD introduced AI-driven rain forecasting tools capable of predicting rainfall down to a 1-kilometer area in a trial for Uttar Pradesh, utilizing weather stations, satellite data, and radar information.

AI algorithms can amalgamate weather predictions with hazard maps and other relevant data to provide a more comprehensive view of disaster risks, enabling timely early warnings.

During natural disasters, an overwhelming amount of data is generated through various means like mobile phones, drones, and satellites. However, this data is often unstructured and requires significant processing to extract useful information. AI excels in quickly analyzing this data, identifying patterns, and providing actionable insights.

For instance, during the Nepal disaster, an AI system cross-referenced public reports of missing individuals with government records and utilized satellite imagery to map destroyed buildings. Drones equipped with thermal cameras assisted rescue teams by detecting human thermal signatures beneath debris, aiding in the rapid identification of potential survivors.

Post-disaster, AI plays a crucial role in assessing damaged infrastructure, identifying isolated communities, and prioritizing the dispatch of essential supplies such as food and medicine. AI-generated risk assessments, satellite imaging, and damage mapping support authorities in pinpointing hazardous areas and formulating improved strategies for future catastrophe risk management.

Successful examples from Jammu and Kashmir, including GIS-based multi-hazard risk mapping and early warning systems, demonstrate a broader trend towards integrating technology into disaster risk reduction efforts.