Autonomous Climate Mitigation System

Abstract

The menace of flood disasters has continued to threaten lives, infrastructure, and economies of the people around the world. The problem of traditional disaster management systems is usually slow prediction and poor coordination in responding to the disaster. In this paper, I have proposed a multi-agent artificial intelligence platform based on an autonomous climate disaster management system (ACDMS), which will predict, monitor, and control the flood disaster events in real time. The system encompasses deep learning architecture to predict floods and monitor disasters, reinforcement learning to allocate resources dynamically and graph-based algorithms to optimize evacuation paths. It further integrates real time weather information, satellite images, geospatial data, and social media contributions to increase situational awareness and decision making. Experimental assessments reveal that ACDMS is more efficient in response, resource optimization and makes coordination better than traditional methods of disaster management. The framework proposed offers a comprehensive and robust solution that is capable of sustaining proactive disaster preparedness, emergency real-time operations and post-disaster recovery planning. The paper also adds to the body of knowledge on intelligent and adaptive flood disaster management with a single architecture that is powered by artificial intelligence.