Article
Mobirescue Optimal Dispatching of Rescue Teams Under Flooding Disasters
Flooding disasters pose significant challenges to emergency response systems due to rapidly changing environments, infrastructure damage, and limited resource availability. Efficient and timely dispatch of rescue teams is critical to minimize loss of life and property. This paper presents Mobirescue, an intelligent and adaptive system designed to optimize the dispatching of rescue teams during flood emergencies. The proposed system integrates real-time data sources such as weather forecasts, water levels, geographic information, and victim locations to dynamically assess disaster severity and prioritize rescue operations. Mobirescue employs advanced optimization techniques and machine learning algorithms to allocate resources effectively, ensuring minimal response time and maximum coverage of affected areas. The system models the disaster environment as a dynamic network and applies route optimization strategies to identify the fastest and safest paths for rescue teams while considering constraints such as road blockages and resource limitations. Additionally, a priority-based scheduling mechanism is introduced to handle critical cases, ensuring that high-risk victims receive immediate attention.Experimental results demonstrate that the proposed approach significantly improves response efficiency compared to traditional dispatch methods, reducing rescue time and enhancing decision-making under uncertainty. The system also provides a scalable and user-friendly interface for emergency management authorities, enabling real-time monitoring and coordination. Overall, Mobirescue offers a robust and intelligent solution for disaster management, contributing to more effective emergency response and improved resilience in flood-prone regions
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