Flooding is one of the most frequent and destructive natural hazards affecting urban areas worldwide, resulting in significant loss of life, property damage, and disruption to critical infrastructure. Traditional flood risk assessment approaches, which rely heavily on hydrological and hydraulic modeling, often suffer from limited data availability, computational intensity, and insufficient adaptability to rapidly changing urban and climatic conditions. Recent advances in artificial intelligence (AI) and machine learning (ML) provide new opportunities to enhance flood prediction and risk mapping by integrating diverse data sources, learning complex spatiotemporal patterns, and generating actionable insights for urban resilience planning. This study proposes an AI-powered framework for flood risk prediction and mapping tailored for urban environments. The framework leverages satellite imagery, topographic data, climate projections, and historical flood records, combined with machine learning algorithms such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and ensemble learning techniques. These models are trained to forecast flood probability, water depth distribution, and extent of inundation under varying rainfall and land-use scenarios. The outputs are visualized through GIS-based dynamic maps that can be directly employed by city planners, emergency response teams, and policy makers. The results highlight the superior performance of AI models compared to conventional flood models, particularly in handling data sparsity and capturing nonlinear interactions between hydrological and urban features. The proposed approach demonstrates how AI-driven predictive mapping can reduce uncertainties, support adaptive decision-making, and ultimately strengthen the resilience of urban communities against flood hazards.
Monowar Hossain Saikat (Mon,) studied this question.