12 citations as recorded by crossref.

1. Time-guided convolutional neural networks for spatiotemporal urban flood modelling Z. Wang et al. 10.1016/j.jhydrol.2024.132250
2. Integrating urban water fluxes and moving beyond impervious surface cover: A review C. Oswald et al. 10.1016/j.jhydrol.2023.129188
3. Enhancing 2D hydrodynamic flood models through machine learning and urban drainage integration H. Taysi et al. 10.1016/j.jhydrol.2025.133258
4. Automatic topology and capacity generation framework for urban drainage systems with deep learning-based land use segmentation and hydrological characterization Q. Zhong et al. 10.1016/j.jhydrol.2024.131766
5. Urban pluvial flood susceptibility mapping based on a novel explainable machine learning model with synchronous enhancement of fitting capability and explainability Z. Wang et al. 10.1016/j.jhydrol.2024.131903
6. City-scale high-resolution flood nowcasting based on high-performance hydrodynamic modelling B. Dong et al. 10.1016/j.ijdrr.2025.105584
7. A new algorithm for generation of urban underground stormwater networks and its application for enhanced urban flood simulation J. Yang et al. 10.1016/j.jhydrol.2025.133571
8. SWMManywhere: A workflow for generation and sensitivity analysis of synthetic urban drainage models, anywhere B. Dobson et al. 10.1016/j.envsoft.2025.106358
9. SWMManywhere: Synthesise Urban Drainage Network Models Anywhere in the World B. Dobson et al. 10.21105/joss.07729
10. Evaluation of drainage efficiency via street inlets under the influence of terrain slope in the course of pluvial urban flood event Y. Xing et al. 10.1016/j.ejrh.2025.102243
11. The influence of road network topology on street flooding in New York City—A social media data approach C. ZUO et al. 10.1016/j.jhydrol.2024.131471
12. Approximation method for the sewer drainage effect for urban flood modeling in areas without drainage-pipe data D. Li et al. 10.3389/fenvs.2023.1134985