11 citations as recorded by crossref.

1. All models are wrong, but are they useful? Assessing reliability across multiple sites to build trust in urban drainage modelling A. Pedersen et al. 10.5194/hess-26-5879-2022
2. A Spatiotemporal Deep Learning Approach for Urban Pluvial Flood Forecasting with Multi-Source Data B. Burrichter et al. 10.3390/w15091760
3. Impact of Spatial Variation and Uncertainty of Rainfall Intensity on Urban Flooding Assessment R. Lin et al. 10.1007/s11269-022-03325-8
4. Fully automated simplification of urban drainage models on a city scale M. Pichler et al. 10.2166/wst.2024.337
5. Information-theoretic sensor placement for large sewer networks G. Crowley et al. 10.1016/j.watres.2024.122718
6. Using multi-event hydrologic and hydraulic signatures from water level sensors to diagnose locations of uncertainty in integrated urban drainage models used in living digital twins A. Pedersen et al. 10.2166/wst.2022.059
7. Interpretable Time Series Models for Wastewater Modeling in Combined Sewer Overflows T. Chiaburu & F. Bießmann 10.1016/j.procs.2024.05.091
8. A simplified approach for the hydrological simulation of urban drainage systems with SWMM A. Farina et al. 10.1016/j.jhydrol.2023.129757
9. What Is the Contribution of Urban Trees to Mitigate Pluvial Flooding? K. Medina Camarena et al. 10.3390/hydrology9060108
10. Exploring the Performance of Ensemble Smoothers to Calibrate Urban Drainage Models Y. Huang et al. 10.1029/2022WR032440
11. Machine Learning‐Based Surrogate Modeling for Urban Water Networks: Review and Future Research Directions A. Garzón et al. 10.1029/2021WR031808