Real-Time Emergency Response Optimization Using Big Data Streams and Reinforcement Learning: A Smart Dispatch Framework for U.S. Disaster Management

Abstract

The increasing frequency and intensity of extreme weather events in the United States have amplified systemic risks to infrastructure, public safety, and emergency response systems. Hurricanes, floods, wildfires, and heatwaves are now occurring with greater unpredictability, placing significant pressure on existing disaster management frameworks. Conventional forecasting and early warning systems, which often rely on static models and limited data inputs, are increasingly inadequate in handling the high-velocity, high-volume, and heterogeneous data generated in real time. These limitations result in delayed decision-making, reduced situational awareness, and suboptimal emergency response outcomes.
This study proposes a unified, data-centric early warning and response optimization framework that integrates big data architectures with deep learning–driven predictive models and reinforcement learning-based decision systems. The framework leverages continuous data streams from sensors, satellite feeds, emergency calls, and social media to enable real-time analysis and adaptive dispatch strategies. By combining predictive intelligence with dynamic resource allocation, the system enhances both anticipatory capabilities and operational responsiveness.
Experimental evaluation demonstrates significant improvements in prediction accuracy, reduction in response latency, and enhanced scalability compared to traditional systems. The proposed framework achieves more precise risk detection at high spatial and temporal resolutions while maintaining robust performance under increasing data loads. These findings highlight the potential of integrating big data and intelligent learning models to transform emergency response systems into proactive, adaptive, and resilient infrastructures. The study contributes a scalable and practical solution for real-time disaster risk detection and optimized emergency dispatch in complex urban environments.