AI-Powered Predictive Analytics for Disaster Response in Smart Cities

Abstract

As urban centers increasingly evolve into smart cities, the challenges of disaster preparedness and response demand intelligent and data-driven strategies. Predictive analytics powered by Artificial Intelligence (AI) has emerged as a vital tool for enhancing resilience and response capabilities in the face of natural and human-made disasters. This paper investigates how AI-driven predictive analytics can support disaster response in smart cities through real-world case studies, including flood forecasting in the Netherlands, earthquake prediction in Japan, and COVID-19 pandemic modeling in South Korea. By examining the methodologies and technologies underpinning these use cases, the research highlights both the potential and limitations of AI applications in disaster risk management. Findings suggest that integrating real-time sensor data, machine learning algorithms, and geospatial information systems (GIS) significantly improves emergency response effectiveness. This study also explores challenges such as data bias, ethical considerations, and infrastructure limitations, providing a roadmap for future research and implementation strategies to strengthen disaster resilience in smart cities.