AI for Disaster Prediction and Management

Abstract

Natural and man-made disasters -- earthquakes, floods, wildfires, industrial accidents, and pandemics -- cause cascading losses in human life, economic activity, and infrastructure that AI-powered prediction and management systems can meaningfully reduce. Effective AI integration across the disaster management cycle (mitigation, preparedness, response, recovery) requires models that provide timely, calibrated predictions from heterogeneous data sources; autonomous coordination of response resources under time-critical constraints; and post-disaster damage assessment at speed and scale that manual survey cannot achieve. This study evaluates nine AI systems across four disaster types -- seismic events, flood events, wildfires, and industrial chemical accidents -- and three management phases: early warning (prediction), real-time response coordination, and post-disaster damage assessment. Systems evaluated include seismic deep learning (PhaseNet, EQTransformer), flood forecasting (LSTM-Hydro, ClimaX-Flood), wildfire spread modelling (FireGNN), satellite damage assessment CNNs (xBD-CNN, ChangeFormer), LLM-powered crisis coordination (CrisisGPT), and multi-modal disaster assessment (MDAS). Evaluation used real disaster event data from 2018-2023 in Europe and the Mediterranean. EQTransformer achieves earthquake P-wave detection with 97.8% recall at 0.4s lead time. LSTM-Hydro provides 72-hour flood peak prediction with RMSE 18.4% below physical model baselines. ChangeFormer achieves 84.6% F1 on post-disaster building damage assessment from Sentinel-2 imagery. CrisisGPT reduces evacuation route planning time by 38.4% but requires mandatory human validation due to hallucination risk. An AI-augmented disaster management framework with human-in-the-loop validation requirements is proposed.