From Nepal to India: How AI Is Aiding Disaster Management

29 Sep 2026

Tags: Environment   Climate Change   Adaptation strategies

Source: The Indian Express

Context: Recent disasters in Nepal demonstrated the growing use of Artificial Intelligence (AI), drones and crowdsourced data for rescue, damage assessment and disaster response.

• AI is increasingly being integrated across the disaster-management cycle, from early warning and hazard mapping to rescue, relief distribution and post-disaster assessment.

AI in Disaster Response: Nepal Example

• An AI-powered web portal in Nepal matched crowdsourced information on missing persons with official lists of the dead and injured, while also mapping damaged buildings.
• Thermal-camera-equipped drones detected human-shaped thermal signatures in debris, helping rescue teams identify locations where people could potentially be trapped.
• These applications demonstrate AI’s ability to process large volumes of real-time, unstructured and geographically distributed information during emergencies.

Why AI Is Useful in Disasters

• Disaster management generates enormous amounts of information from weather agencies, satellites, smartphones, drones, telecom networks and other sources.
• AI can process this information rapidly using text, speech, images and video, helping authorities make time-sensitive decisions.
• This is particularly valuable because time is a critical factor in early warning, rescue and relief operations.
• Disaster management is consequently expanding beyond traditional government agencies, with technology companies, telecom providers and satellite companies becoming important participants.

AI for Early Warning

• AI can improve weather forecasting by analysing large volumes of historical weather data and learning patterns in atmospheric behaviour.
• Conventional forecasting relies heavily on complex physics-based models, while highly localised and time-specific forecasts require substantial computing resources and can involve greater uncertainty.
• Once adequately trained, AI models can generate hyperlocal forecasts much faster, potentially enabling near-real-time warnings.
• Google’s Flood Hub uses data from major weather agencies and predictive models to forecast floods; it was first used during Indian floods in 2018 and can provide advisories up to seven days in advance.

From Forecast to Risk Assessment

• A weather forecast alone does not indicate the actual level of risk; it needs to be combined with hazard and vulnerability information.
• AI can integrate datasets from multiple agencies to generate a broader threat assessment, helping authorities issue more targeted warnings.
• Thus, AI can connect the stages of forecasting → hazard mapping → risk assessment → early warning.

AI in Response, Rescue and Relief

• Smartphones and drones have transformed citizens and private operators into important sources of real-time visual information, generating large datasets during disasters.
• AI can process information from hundreds of sources and extract useful information from unstructured data, including information available in local languages.
• This can support rescue teams in identifying affected areas and prioritising locations requiring immediate intervention.
• AI-enabled tools can also assess road conditions, identify potential helicopter landing sites and help local authorities prioritise food and relief distribution.

AI Across the Disaster-Management Cycle

• Before disaster: Weather forecasting, early warning and hazard mapping.
• During disaster: Processing crowdsourced information, drone imagery and other real-time data for rescue and response.
• After disaster: Assessing damage, road accessibility and infrastructure conditions and supporting prioritisation of relief distribution.

Limitations and Human Role

• AI does not replace scientists, disaster-response personnel, humanitarian workers and local authorities; its effectiveness depends on their expertise and implementation capacity.
• Reliable disaster management ultimately requires strong institutions, effective governance frameworks and human expertise.
• AI therefore introduces not only new technological capabilities but also new responsibilities, particularly regarding the reliability, fairness and appropriate use of AI-generated information.