From Nepal to India: How AI Is Aiding Disaster Management

During the recent Nepal disaster, AI-enabled tools were used to match crowdsourced data on missing persons with official lists, identify damaged buildings and assist rescue operations. The episode highlighted the growing role of AI, drones, satellite imagery and digital platforms in disaster preparedness, response and recovery.

Growing Use of AI in Disaster Management

• AI can rapidly process large volumes of satellite images, videos, speech, text and sensor data.
• This allows disaster agencies to obtain near-real-time situational awareness and respond faster.
• AI-enabled tools are increasingly used for early warning, hazard mapping, forecasting, rescue planning and post-disaster assessment.

Early Warning and Forecasting

• AI can generate faster and more localised forecasts by learning from large historical weather datasets.
• It can complement conventional physics-based weather models, especially for hyperlocal predictions.
• AI tools can combine weather forecasts with hazard maps to produce risk assessments and early warnings.
• Google Flood Hub is an example of an AI-supported platform used for flood forecasting.

Response, Relief and Rescue

• AI can help process unstructured information from multiple sources and convert it into actionable rescue intelligence.
• Drones equipped with thermal cameras can detect human heat signatures under debris.
• AI can support identification of missing persons, damaged infrastructure and priority rescue locations.
• It can also improve coordination among rescue teams by filtering and organising large volumes of incoming data.

Post-Disaster Use

• AI tools can help locate cut-off settlements, damaged roads and possible helicopter landing sites.
• They can support governments in damage assessment, relief distribution and reconstruction planning.
• Satellite and drone imagery can be processed quickly to identify areas needing urgent assistance.

Role of Private Sector

• Technology firms, telecom companies and satellite operators are increasingly becoming part of disaster-management ecosystems.
• Platforms developed by firms such as Google and specialised AI companies can supplement government capacity in forecasting, mapping and communication.

Key Challenges

• AI-based disaster management still depends heavily on quality data, reliable connectivity and trained human experts.
• AI tools must be integrated with strong institutions, governance systems and humanitarian response mechanisms.
• There are also concerns around accountability, equitable access and over-reliance on automated systems.

AI is becoming an important force multiplier in disaster management by improving early warning, situational awareness, rescue efficiency and post-disaster recovery, but it works best when combined with strong human and institutional capacity.