AN INTELLIGENT PREDICTIVE FRAMEWORK FOR FLOOD AND FIRE RISK ASSESSMENT AND DISASTER DECISION SUPPORT
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Abstract
The AI-driven multi-hazard disaster management system developed in this project effectively bridges the gap between predictive intelligence and ground-level emergency logistics. By integrating live meteorological data from the OpenWeather API with robust machine learning algorithms—specifically the serialized Random Forest classifier—the system accurately forecasts urban flood and forest fire probabilities in real-time. Furthermore, by moving beyond traditional static graph algorithms and integrating the dynamic Google Maps API, the evacuation routing module ensures that citizens are provided with safe, traffic-aware navigation to nearby shelters while actively avoiding predicted hazard zones. The inclusion of a deterministic, rule-based Resource Allocation Engine and a secure, 3-tier Role-Based Access Control (RBAC) architecture streamlines communication and supply chain coordination among Citizens, NGOs, and Government Officials. Overall, this data-driven ecosystem minimizes response latency, optimizes relief distribution, and establishes a robust, highly scalable technological framework for modern crisis management.
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Double-Blind Peer Review
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CC BY-NC 4.0
Volume/Issue:
Volume 02, Issue 9
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2
Article Type:
Research Article
Publication Date:
Sep 15 2026
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J, A. & K, C. (2026). An Intelligent Predictive Framework for Flood and Fire Risk Assessment and Disaster Decision Support. International Journal of Science, Strategic Management and Technology, 02(9), 1-9. https://doi.org/10.55041/ijsmt.v2i9.008
J, AASHA, and CHITHARTHANI K. "An Intelligent Predictive Framework for Flood and Fire Risk Assessment and Disaster Decision Support." International Journal of Science, Strategic Management and Technology, vol. 02, no. 9, 2026, pp. 1-9. doi:https://doi.org/10.55041/ijsmt.v2i9.008.
J, AASHA, and CHITHARTHANI K. "An Intelligent Predictive Framework for Flood and Fire Risk Assessment and Disaster Decision Support." International Journal of Science, Strategic Management and Technology 02, no. 9 (2026): 1-9. https://doi.org/https://doi.org/10.55041/ijsmt.v2i9.008.
References
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- Roy, , & Banerjee, T. (2019). Optimizing Disaster Relief Resource Allocation using Linear Programming. Computers & Industrial Engineering, Elsevier, 130, 54–65.
- Zhang, , & Li, Q. (2020). Forest Fire Spread Simulation using Cellular Automata. ACM Transactions on Environmental Modelling, 25(3), 1–18.
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- Mehta, , & Kumar, R. (2023). Multi-Hazard Disaster Management using AI and Big Data. AI and Society Journal, Springer, 38(1), 145–162.
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This article has undergone plagiarism screening and double-blind peer review. Editorial policies have been followed. Authors retain copyright under CC BY-NC 4.0 license. The research complies with ethical standards and institutional guidelines.
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