AI-Driven Integrated System for Climate Disaster Mitigation: An Early-Warning Architecture for India
Keywords:
Artificial Intelligence, Climate Disaster Mitiga- tion, Early Warning Systems, Machine Learning, Deep Learning, Multi-hazard Forecasting, India, Vulnerability MappingAbstract
India faces recurrent climate hazard floods, cy- clones, wildfires, heatwaves, and droughts whose intensity and frequency have accelerated under anthropogenic climate change. Although conventional Early Warning Systems (EWS) have significantly improved in recent decades, they continue to face limitations in temporal resolution, spatial accuracy, and impact- based forecasting. This paper presents a comprehensive AI- driven multi-hazard EWS architecture for India, integrating meteorological, hydrological, and socio-economic data using Ma- chine Learning (ML) and Deep Learning (DL) techniques. By fusing multi-source satellite imagery, gridded observations, and a curated Vulnerability Database, the system aims to improve predictive precision and deliver hyperlocal alerts. The frame- work emphasizes hybrid architectures Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Transformers to achieve scalable, real-time, and explain- able hazard predictions. The proposed design aligns with India National Disaster Management Plan (NDMP-2023) and the UN Sustainable Development Goal 13 on Climate Action.
