Artificial Intelligence In Disaster Management

Source: Indian Express
GS III: Science and Technology, Disaster management, environmental conservation and Climate Change

Why in the News?

The recent Nepal disaster highlighted the growing role of Artificial Intelligence (AI) in disaster management.

News in Brief

• AI-powered robots and drones are helping identify missing persons, detect survivors and map damaged buildings during disasters.
• Satellite imagery and thermal cameras assist in locating trapped people, assessing damage and identifying high-risk areas.
• The experience shows how emerging technologies can strengthen disaster preparedness, response and recovery in India and other disaster-prone countries.

Growing role of AI in Disaster Management

• Disaster management is no longer limited to government agencies and traditional rescue teams.
• Technology companies, satellite service providers and AI developers are increasingly contributing to disaster preparedness and response.
• AI tools are being used across different stages of disaster management;
  • Disaster preparedness-  Predicting extreme weather events and identifying vulnerable areas.
  • Early warning- Analysing weather patterns and generating timely alerts.
  • Response and rescue- Locating trapped persons, mapping damaged structures and assisting rescue teams.
  • Recovery- Assessing infrastructure damage, identifying affected households and supporting the distribution of relief materials.
• The major advantage of AI lies in its ability to process large volumes of data using natural language processing, speech recognition and real-time analysis.
• This enables disaster management authorities to make quicker and more informed decisions.

AI- Based Early Warning Systems

• Traditional weather forecasting relies on numerical weather prediction models, which solve complex physical equations to understand atmospheric conditions.
• These models require substantial computing resources and time.
• Forecast uncertainty generally increases as the prediction period becomes longer.
• AI-based weather forecasting offers an additional approach.
  • AI models are trained on historical weather observations and past weather patterns.
  • They identify relationships between atmospheric conditions and likely weather developments.
  • Once trained, these models can generate forecasts more rapidly and support near-real-time prediction.
  • AI can process satellite imagery, weather station readings and other datasets to identify emerging risks.
• For example, Google DeepMind’s GraphCast uses machine learning to generate global weather forecasts.
  • Its first version was introduced in 2023 and demonstrated the potential to produce forecasts up to 10 days ahead.
• However, AI forecasts are not a replacement for conventional meteorological models.
• Combining AI predictions with physics-based models and expert assessment can improve forecasting reliability.

AI for Hazard Mapping and Risk Assessment

• AI can integrate multiple datasets to prepare detailed hazard maps and assess disaster risks.
• It can analyze satellite images, topographical information, rainfall patterns, population density and infrastructure data to identify areas vulnerable to floods, landslides and other disasters.
• Applications include;
  • Identifying flood-prone settlements and areas exposed to landslides.
  • Mapping buildings, roads and other infrastructure damaged during disasters.
  • Assessing the number of people potentially affected by a hazard.
  • Supporting authorities in prioritising evacuation, rescue and relief operations.
• AI-based hazard mapping can also help governments identify high-risk areas before a disaster occurs, allowing them to plan evacuation routes and allocate resources in advance.

AI in disaster response, relief and rescue

• AI is increasingly assisting rescue teams in situations where conventional search and rescue operations are difficult or dangerous.
• During the recent Nepal disaster, AI-powered robots were reported to have helped identify missing persons, detect human presence beneath debris and map damaged buildings.
• Other emerging applications include,
  • Thermal imaging drones- Detecting human body heat beneath rubble or in areas with limited visibility.
  • Autonomous and AI-assisted drones- Surveying damaged areas and transmitting aerial images to rescue teams.
  • Satellite imagery- Identifying damaged infrastructure, blocked roads and affected settlements.
  • Natural Language Processing (NLP)- Analysing social media posts, emergency messages and distress calls to identify urgent needs and locations.
• AI can process information from multiple sources and extract meaningful data more efficiently, helping authorities make decisions under time pressure.
• However, rescue operations continue to depend on trained personnel, physical access, local knowledge and coordination among agencies.

AI in Post- Disaster Recovery

• The role of AI continues even after the immediate rescue phase.
• Specialised tools can help authorities:
  • Identify damaged buildings and assess the extent of destruction.
  • Locate people who remain disconnected or stranded after a disaster.
  • Assess road conditions and identify accessible routes for relief operations.
  • Prioritise the distribution of food, medicines, shelter and other essential supplies.
  • Support reconstruction planning through satellite imagery and damage assessments.
• These applications can improve the speed and coordination of recovery efforts, particularly when disasters affect large geographical areas.

Important Initiatives and Applications in India

• Coalition for Disaster Resilient Infrastructure (CDRI)- Promotes disaster-resilient infrastructure and international cooperation.
• National Disaster Management Plan- Provides the framework for prevention, mitigation, preparedness, response, recovery and reconstruction.
• India Meteorological Department (IMD)- Provides weather forecasts and warnings for cyclones, heavy rainfall and other extreme weather events.
• National Centre for Medium Range Weather Forecasting (NCMRWF)- Undertakes numerical weather prediction and related research to improve weather forecasting.
• Indian National Centre for Ocean Information Services (INCOIS)- Provides ocean-related information, including tsunami early warnings and ocean state forecasts.

The integration of AI with satellite technology, weather forecasting systems and disaster response institutions can improve India’s disaster preparedness.

Challenges in using AI for Disaster Management

• Despite its potential, AI-based disaster management faces several challenges.
• Incomplete, outdated or inaccurate datasets can lead to unreliable predictions, while the digital divide limits access to AI-based warnings in remote and vulnerable communities.
• High costs associated with computing infrastructure, sensors and trained personnel pose financial constraints.
• Further, AI models trained in one region may not perform accurately in areas with different geographical and climatic conditions.
• The use of personal information, location data and images during emergencies also raises privacy concerns.
• Additionally, AI systems may generate false alarms or fail to detect certain hazards, making human oversight, accountability and verification essential.
• Therefore, excessive dependence on AI must be avoided, and traditional warning systems and human expertise should continue to complement technological solutions.

Way Forward and Conclusion

The effective integration of AI into disaster management requires reliable, region-specific datasets, investment in digital infrastructure, capacity building and public-private partnerships. AI-based early warnings must reach vulnerable communities through local languages and accessible communication systems, while human oversight, data privacy and accountability remain essential.

By combining AI with human expertise, institutional coordination and community participation, India can strengthen disaster preparedness, improve response and recovery, and minimize the loss of lives and livelihooods.

UPSC Prelims and Mains Practice Question

With reference to the application of Artificial Intelligence in disaster management, consider the following statements:

1. AI-based weather forecasting models can process historical weather data to generate forecasts and support early warning systems.
2. Thermal imaging drones can assist rescue teams in detecting human presence in disaster-affected areas.
3. AI-based disaster management systems can completely replace human intervention and institutional coordination during rescue operations.

Which of the statements given above is/are correct?

(a) 1 and 2 only

(b) 2 and 3 only

(c) 1 and 3 only

(d) 1, 2 and 3

Answer: (a) 1 and 2 only

Mains Practice Question 

Q)  Artificial Intelligence has the potential to transform disaster management from a reactive approach to a predictive and preventive system. Discuss its applications, challenges and the measures required for its effective integration into India’s disaster management framework. (250 words)

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