An Explainable AI-Driven Early Warning System for Extreme Weather Risk Assessment and Disaster Preparedness

Abstract:- Extreme weather events have become increasingly frequent and destructive due to climate change, rapid urbanization, and environmental degradation, resulting in substantial threats to human lives, critical infrastructure, and socio-economic stability. Although conventional Early Warning Systems (EWSs) provide timely hazard forecasts, they generally rely on threshold based prediction models and operate as black-box decision-support tools, limiting transparency and reducing confidence among emergency managers. Furthermore, most existing systems focus primarily on hazard prediction while providing limited support for impact-oriented disaster preparedness and risk-informed decision-making. To address these challenges, this paper proposes an Explainable AI-driven Early Warning System (XAI-EWS) for multi-hazard extreme weather risk assessment and disaster preparedness. The proposed framework integrates heterogeneous environmental observations obtained from weather stations, satellite imagery, remote sensing platforms, Internet of Things (IoT) sensors, radar measurements, and historical meteorological records into a unified data fusion architecture. A hybrid deep learning model combining Bidirectional Long Short-Term Memory (Bi-LSTM) and Extreme Gradient Boosting (XGBoost) is employed to capture complex temporal dependencies and nonlinear relationships among meteorological variables. To improve model transparency, SHapley Additive exPlanations (SHAP) are incorporated to quantify the contribution of individual environmental features influencing weather-risk predictions. The predicted hazard information is further combined with exposure and vulnerability indicators to estimate a dynamic risk score and generate preparedness recommendations that support emergency response planning. Comparative evaluation demonstrates that the proposed framework offers improved prediction reliability, enhanced interpretability, and more effective decision support than conventional AI-based warning systems. The proposed explainable framework has the potential to strengthen operational disaster preparedness while promoting transparent and trustworthy artificial intelligence for climate resilient emergency management.

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