AI-driven Multi-Hazard Prediction and Decision Support System for Flood and Fire Disaster Management
Keywords:
Artificial Intelligence, Decision support system, Early warning system, Evacuation Routing, flood prediction, Forest fire prediction, Multi-hazard management, Random forest, Resource allocation, Role-Based access controlAbstract
Accurate risk assessment and coordinated response strategies are necessary for effective disaster management to reduce loss and damage. An AI-powered multi-hazard decision support system for managing forest fires and floods is presented in this paper. While real-time environmental parameters temperature, humidity, rainfall, wind speed, and atmospheric pressure are continuously gathered through a weather API to facilitate live risk assessment, a predictive model that can recognise risk patterns is trained using historical disaster datasets. Based on predetermined thresholds, the system classifies risk into low, medium, and high levels and calculates the likelihood of disasters. Automated alarms are produced, safe shelter evacuation routes are calculated, and vital resources are allocated as efficiently as possible in high-risk scenarios. In order to facilitate organised communication and coordinated response, the platform also includes role-based access for individuals, government officials, and non-governmental organisations. The suggested approach improves situational awareness and facilitates data-driven disaster preparedness and response by combining prediction, early warning, evacuation planning, and resource management into a single framework.
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