AI-Driven Decision Systems for Real-Time Disaster Prediction

Abstract

Both natural and man-made disasters are very impactful to the surroundings, infrastructure, and human life. Proper and timely forecasting of disasters is essential in the reduction of the disaster. The conventional disaster prediction models are based on analysis of previous history and simple statistical methods which could be not capable of offering real-time and adaptive decision-making options. The present paper includes an in-depth research concerning AI-based decision systems of real-time disaster predictions that combine the latest machine learning (ML), deep learning (DL), and real-time sensor networks. We suggest an approach based on multi-layered which involves combining real-time data collection, AI-enhanced predictive analytics, and automated decision-making to improve disaster preparedness and response. The system resorts to ensemble learning, recurrent neural networks (RNNs), and spatiotemporal modeling and manages to predict the occurrence of a flood, earthquake, wildfire, and storm with high accuracy. The system is shown in one of the case studies that use actual real-time sensor data on the environment and satellite images to prove the efficiency of the system. It is shown that there is substantial increase in accuracy of prediction and response time over traditional systems. The given strategy is focused on scalability, flexibility, and resilience to different disaster risks. In addition, the combination of AI and Internet of Things (IoT) and Geographic Information System (GIS) allows developing a real-time decision support system that can support the government agencies, emergency responders, and communities with making proactive and data-driven decisions. The study indicates the possibilities of AI-powered systems in shifting disaster management to a predictive instead of a reactive and prevention system.