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

Urban flooding has become a growing concern for many cities due to accelerating urbanisation, changing weather, and drainage system aging. Earlier studies of floods have taken primarily the traditional process-based approach to predicting urban floods, offering limited exploration of recent advancements in AI-driven, real-time, and community-integrated approach, which this paper brings into focus. This paper reviews how flood prediction has improved over the last two decades. It begins by reviewing physical process-based models (PPBMs), which often could not handle the fast changes in cities. New tools like geographic information systems (GIS), light detection and ranging (LiDAR), and satellite images helped improve flood mapping and planning. A big shift came with the use of AI and machine learning. They have made predictions faster, smarter, and more accurately. They allow many types of data, like weather information, sensor data, and social media (crowdsourcing) data. Recently, new tools like Internet of Things devices, deep learning, and hybrid models have brought even more progress. However, there are still challenges. Many cities still do not have the data, sensors, or systems needed to use these tools. Many models work on their own, not linked with city planning or community efforts. Flood solutions must now be more than just technical. Future systems should combine AI, hydrodynamics, GIS, and real-time monitoring, adapt to city change, and include input from communities. Open-source tools, public education, and better planning are also needed to make cities safer and more resilient to costly floods.