AI-Driven Early Warning Systems for Extreme Climate Events (Floods, Heatwaves, Cyclones)
Keywords:
Artificial intelligence, Climate informatics, Cyclone track forecasting, Deep learning, Disaster risk reduction, Early warning systems, Flood prediction, Heatwave detection, IoT, LSTMAbstract
Climate change has intensified the frequency and severity of extreme weather events, including floods, heatwaves, and cyclones, posing unprecedented challenges to human societies and ecosystems. Traditional Numerical Weather Prediction (NWP) models, while foundational, often suffer from high computational costs and limited skill at local spatial scales. The advent of Artificial Intelligence (AI) — particularly deep learning, ensemble methods, and hybrid physics-informed neural networks — has ushered in a new paradigm for climate event prediction and early warning. This paper presents a comprehensive review of AI-driven Early Warning Systems (EWS) for floods, heatwaves, and cyclones. They examine the data ecosystems (satellite, IoT, reanalysis), model architectures (LSTM, CNN, GNN, Transformers, diffusion models), and system integration strategies employed globally. Performance comparisons are presented, key research gaps identified, and future directions proposed. Our analysis demonstrates that AI-enhanced EWS can reduce false negative rates by up to 30% compared with conventional methods, improve lead time by several hours, and enable equitable, low-bandwidth dissemination in resource-constrained regions. The review synthesises over 30 key studies and situates them within a unified framework to guide future research and operational deployment.
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