Articles | Volume 25, issue 11
Hydrol. Earth Syst. Sci., 25, 5839–5858, 2021
https://doi.org/10.5194/hess-25-5839-2021
Hydrol. Earth Syst. Sci., 25, 5839–5858, 2021
https://doi.org/10.5194/hess-25-5839-2021

Research article 11 Nov 2021

Research article | 11 Nov 2021

Modeling and interpreting hydrological responses of sustainable urban drainage systems with explainable machine learning methods

Yang Yang and Ting Fong May Chui

Data sets

Flow and Rainfall Data used for SHC Headwatershed SWMM Calibration EPA https://doi.org/10.23719/1378947

Model code and software

stsfk/ExplainableML_SuDS: (v1.0) stsfk https://doi.org/10.5281/zenodo.5652719

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Short summary
This study uses explainable machine learning methods to model and interpret the statistical correlations between rainfall and the discharge of urban catchments with sustainable urban drainage systems. The resulting models have good prediction accuracies. However, the right predictions may be made for the wrong reasons as the model cannot provide physically plausible explanations as to why a prediction is made.