AI for Disaster Resilience: Modeling and Forecasting Urban Flood Risks

Authors- Surya Narayana

Abstract--Urban flooding is one of the most devastating and increasingly frequent natural disasters, exacerbated by climate change, unplanned urbanization, and aging infrastructure. Traditional flood prediction and response mechanisms are often constrained by limited data, slow reaction times, and a lack of real-time insight. Artificial Intelligence (AI) offers a transformative approach to enhancing urban disaster resilience by enabling accurate, data-driven modeling and forecasting of flood risks. This paper explores the foundational technologies underpinning AI applications in urban flood prediction, including machine learning algorithms, remote sensing, geospatial analytics, and real-time data integration. It presents practical use cases such as early warning systems, flood mapping, and smart infrastructure adaptation. Case studies from cities such as Jakarta, New York, and Mumbai illustrate how AI-driven models have improved flood forecasting accuracy, informed policy decisions, and mitigated damage. Ethical and regulatory challenges, including data privacy, digital equity, and governance, are also examined. The paper concludes with an outlook on future innovations, including federated AI, digital twins of cities, and citizen-generated data platforms. AI is poised to become a cornerstone of urban flood resilience strategies, empowering cities to predict, prepare for, and adapt to flood events in a rapidly changing world.

DOI: /10.61463/ijset.vol.13.issue2.382