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Application of AI in Urban Flash Flood Risk Assessment: From Real-time Warning to Resilience Planning
- 1 School of Water Conservancy and Environment, UNIVERSITY OF JINAN, Jinan City, 250000, China
* Author to whom correspondence should be addressed.
Abstract
The rapid advancement of artificial intelligence (AI) has revolutionized urban flash flood risk assessment, offering transformative solutions from real-time warning systems to long-term resilience planning. Coastal and low-lying urban areas, housing over 40% of the global population, face escalating flood risks due to climate change, sea-level rise, and intensified extreme weather. Traditional flood modeling, reliant on physical parameters, struggles with computational inefficiency and data scarcity. AI-driven approaches, particularly deep learning (DL) and neural networks address these gaps by leveraging multi-source data fusion, dynamic prediction, and reinforcement learning (RL) to enhance accuracy and efficiency. Techniques such as convolutional neural networks (CNNs) and U-Net architectures enable automated flood mapping using satellite and sensor data, while hybrid models integrating hydrodynamic simulations with machine learning (ML) improve inundation forecasting. Despite progress, challenges persist, including data quality in developing regions, model generalizability, and ethical concerns in AI deployment. This review highlights AI's potential to bridge technical gaps, optimize emergency responses, and inform resilient urban planning while underscoring the need for robust datasets, interdisciplinary collaboration, and ethical frameworks to ensure equitable and sustainable flood risk management.
Keywords
AI, urban sustainable development, storm surge, risk assessment
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Cite this article
Zhou,S. (2025). Application of AI in Urban Flash Flood Risk Assessment: From Real-time Warning to Resilience Planning. Applied and Computational Engineering,150,9-14.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 3rd International Conference on Software Engineering and Machine Learning
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