Evolution from the physical process-based approaches to machine learning approaches to predicting urban floods: a literature review

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Literature review reveals machine learning improves flood predictions in urban areas, indicating a need for integrated community approaches.

Machine learning approaches have significantly advanced urban flood predictions compared to traditional methods.
Adoption of technologies such as AI, GIS, and IoT devices has enhanced data collection and analysis for flood forecasting.
Challenges remain in integrating flood prediction tools with city planning and community engagement.
Future solutions require a combination of AI, hydrodynamics, and community input to increase urban resilience against floods.

Anik et al. (2025) studied this question.

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Literature review reveals machine learning improves flood predictions in urban areas, indicating a need for integrated community approaches.