An Explainable AI-Driven Early Warning System for Extreme Weather Risk Assessment and Disaster Preparedness
Volume: 15 - Issue: 07 - Date: 30-07-2026
Approved ISSN: 2278-1412
Published Id: IJAECESTU504 | Page No.: 111-120
Author: Shruti Khare
Co- Author: Mayur Singi
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.
Key Words:-Explainable Artificial Intelligence, Early Warning System, Extreme Weather, Disaster Preparedness, Risk Assessment, SHAP, Bi-LSTM, XGBoost, Decision Support, Climate Resilience.
Area:-Engineering
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