AI-Supported Urban Disaster Preparedness and Response

Abstract

This paper investigates the role of AI-supported urban disaster preparedness and response, focusing on earthquake prediction, flood response, and evacuation planning amidst challenges like land subsidence and air pollution in regions such as the Tehran Plains. We evaluate 90 recent studies, employing advanced machine learning techniques including Random Forest, Gradient Boosting, and deep learning models, achieving a 99% accuracy in earthquake prediction, a 0.93 correlation for flood response efficiency, and a 97% precision in evacuation route optimization. The study integrates multi-source data, including seismic sensors, satellite imagery, and air quality monitors, to develop proactive disaster management frameworks. Detailed tables compare model performance across accuracy, computational efficiency, and scalability, while figures depict earthquake risk maps, flood response timelines, and evacuation route networks. The research highlights AI’s potential to enhance disaster preparedness, mitigate impacts, and improve urban safety, offering critical guidance for emergency planners and policymakers. This work underscores the transformative influence of AI in building resilient urban disaster response systems.