AI‑Driven Disaster Management Post‑Disaster Management (Amendment) Act, 2025 – Key Initiatives and Institutional Roles The Disaster Management (Amendment) Act, 2025 has catalysed a technology‑centric approach to the entire disaster management cycle – from preparedness to mitigation. Central agencies are now embedding AI and ML tools in forecasting, risk mapping and decision support. Key Developments (Bullet Points) National Disaster Database – a unified repository of risk assessments, mitigation plans and real‑time disaster data, mandated by the 2025 amendment. AI‑enabled weather forecasting – the IMD now uses AI/ML models for seven‑day predictions, including flood simulations and cyclone tracking under Mission Mausam . Web‑DCRA & DSS tool – a web‑based Dynamic Composite Risk Atlas and Decision Support System developed by the NDMA , successfully employed during Cyclones Biparjoy and Michaung. Flood Hazard Atlas – created by the National Remote Sensing Centre (NRSC) for flood‑prone states (e.g., West Bengal, Bihar) and less‑affected states, providing high‑resolution flood risk layers. AI‑based flood forecasting by CWC – the Central Water Commission launched pilot AI/ML short‑range flood models in 2025 and now offers seven‑day advisory forecasts on its portal aff.india-water.gov.in . Avalanche forecasting by DRDO – the Defence Research and Development Organisation is developing AI‑driven avalanche hazard forecasting, control structures and an autonomous avalanche forecasting system for the Himalayan region. Important Facts • The AI integration covers major hazards – floods, cyclones and avalanches – across diverse geographies. • Real‑time data feeds from satellite, radar and ground stations feed the AI models, enhancing prediction accuracy up to seven days. • Funding details for AI‑driven disaster initiatives are not centrally collated, indicating a need for better financial tracking. • Training and awareness programmes for officers and volunteers are listed in the annexure, underscoring capacity‑building efforts. UPSC Relevance Understanding the shift to AI‑enabled disaster management is crucial for GS2 (Polity) – the legal and institutional framework, GS3 (Technology & Economy) – the role of emerging technologies in public governance, and GS4 (Ethics) – the ethical implications of data‑driven decision‑making in life‑saving operations. Questions may probe the effectiveness of the NDMA tools, inter‑agency coordination, or the challenges of integrating AI in a federal structure. Way Forward Establish a centralised fund tracking mechanism for AI‑based disaster projects to ensure transparency and optimal resource allocation. Scale up AI pilots to cover all river basins and coastal zones, incorporating local knowledge and community‑based data. Institutionalise regular training modules for disaster responders, emphasizing AI literacy and ethical data use. Strengthen inter‑agency data sharing platforms to create a seamless, real‑time national disaster monitoring network. These steps will consolidate India’s move towards a resilient, technology‑forward disaster management system, aligning with global best practices and the nation’s commitment to sustainable development.