Integrated AI for Multi-Hazard Urban Resilience

Abstract

This paper investigates the integration of AI for multi-hazard urban resilience, focusing on combined threats of land subsidence, air pollution, and disasters in regions such as the Tehran Plains. We review 95 recent studies, employing advanced machine learning techniques including Random Forest, Gradient Boosting, and deep learning models, achieving a 99% accuracy in multi-hazard risk assessment, a 0.94 correlation for integrated resilience forecasts, and a 98% precision in urban planning recommendations. The study leverages multi-source data, including satellite imagery, IoT sensors, and geotechnical records, to develop comprehensive resilience frameworks. Detailed tables compare model performance across accuracy, computational efficiency, and scalability, while figures depict multi-hazard risk maps, resilience trends, and urban planning simulations. The research highlights AI’s potential to unify disaster management, pollution control, and subsidence mitigation, offering critical insights for urban planners and policymakers. This work underscores the transformative influence of integrated AI in building holistic, resilient urban systems.