MULTI-AGENT Disaster Management System

Abstract

Natural disasters continue to create significant challenges for emergency response teams due to delays in incident assessment, inefficient allocation of rescue resources, and lack of coordinated decision making. Conventional disaster management systems mainly depend on manual operations, making it difficult to respond effectively when multiple emergencies occur simultaneously. This paper presents a Multi-Agent Disaster Management Simulator that automates the disaster response process using intelligent software agents, machine learning, graph-based routing, and generative artificial intelligence. The proposed system consists of four autonomous agents responsible for incident detection, resource allocation, rescue coordination, and performance evaluation. A Random Forest model predicts disaster severity using environmental and demographic features, while the A* search algorithm identifies the shortest rescue path between response teams and disaster locations. Google Gemini API is integrated to generate human-readable explanations for resource allocation decisions. A React-based dashboard provides live visualization of incidents, rescue routes, and performance metrics through WebSocket communication. Experimental results demonstrate that the proposed approach improves response efficiency, reduces manual intervention, and provides better transparency during emergency management. The simulator also supports offline execution through rule-based fallbacks, making it suitable for academic research, training, and disaster response simulations.