Deep Reinforcement Learning for Addressing Interoperability Challenges in Disaster Management Systems

This research sheds light toward the use of Deep Reinforcement Learning (DRL) strategy to solve interoperability issues in disaster management systems. The overall performance of the proposed DRL-based framework is deemed to be superior to the conventional rule-based systems in terms of response time, coordination efficiency, and resource utilization, clocking up the enhancements of 33%, 20% and 20%, respective. By way of simulation and actual re-Creation to emerge as a proper model-based planning format for successful disaster management, the efficacy of DRL framework is evident. It also aligns well with the concern by pointing out that DRL holds the potential for revolutionizing the process of disaster management and presents a sound efficient and effective solution necessary for the management of the modern difficulties.