ABSTRACT Rapid urban flood mapping is crucial for timely risk alerts and emergency relief. Machine learning (ML)‐based mapping models emerge as a promising approach for fast, accurate inundation forecasts. However, current ML models often use precipitation features as inputs and predict maximum flood depth for all grid cells of a specific region simultaneously. This special design improves their prediction efficiency but limits their application in new regions. This study aims to create a highly adaptable, rapid urban maximum flood water depth mapping model based on the random forest regression algorithm and the extreme gradient boosting algorithm. Our mapping model additionally incorporates terrain and land‐use features, besides the precipitation feature, as input variables and generates the maximum water depth only for a grid cell in each mapping. Thus, it can be unchangeably applied to the grid cells in a new area when the model is fully trained. In the case study of Shenzhen, China, our ML‐based mapping model demonstrated excellent mapping ability in both training and validation sets. The coefficient of determination ( R 2 ) is consistently greater than or close to 95%. Furthermore, it revealed good generalization ability when directly applied to a new rainfall event ( R 2 = 0.875) and a new area ( R 2 = 0.810). Meanwhile, the time cost of the mapping model is less than 3 s, meeting the requirement for real‐time mapping. These results indicate that this highly adaptable model, once appropriately trained, can be applied to rapid urban flood severity mapping, which significantly reduces its use cost in urban flood management.
Li et al. (Wed,) studied this question.