AI Assisted Real Time Response Systems for National Emergency Preparedness and Disaster Management

This paper focuses on a real-time response system supported by AI developed to improve national emergency readiness and disaster response. The proposed system includes decision support for disaster response through real-time data processing with the application of machine learning/deep learning algorithms applied to the data collected from IoT sensors, satellite images and social media commenting. Hence, the proposed system optimizes the resources, the probability of the forecasts and decisions made through the integration of linear programming, probabilistic models, and stochastic optimization. Having positively noted the effect of the system on response time of aggregated queries, the Level 2 priority simulations also revealed that the offered optimization of resource allocation is comprised at 90%, while the deep learning statistical information of disaster prediction conveys an important accuracy of 89%. The Resource management policies ensured effective manner of delivering personal and operational necessities, which flexibly changed depending on the situation. This research also shows the ability of the system to improve Disaster management by increasing efficiency in managing large-scale emergencies.