Articles | Volume 25, issue 11
https://doi.org/10.5194/hess-25-5839-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-5839-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Modeling and interpreting hydrological responses of sustainable urban drainage systems with explainable machine learning methods
Yang Yang
Department of Civil Engineering, University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China
Ting Fong May Chui
CORRESPONDING AUTHOR
Department of Civil Engineering, University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China
Related authors
No articles found.
Mengxiang Zhang and Ting Fong May Chui
Hydrol. Earth Syst. Sci., 29, 2655–2695, https://doi.org/10.5194/hess-29-2655-2025, https://doi.org/10.5194/hess-29-2655-2025, 2025
Short summary
Short summary
This study introduces a multiagent socio-hydrologic framework for city-, inter-city-, and watershed-scale integrated green infrastructures (GIs) and water resource management. Applied to the Upper Mississippi River basin, it explores GI-driven water-sharing dynamics in a watershed. It identifies four city-scale water use patterns and characterizes cost and equity on broader scales, thereby enhancing comprehension of the role of GIs in water resource management and aiding decision-making.
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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.
This study uses explainable machine learning methods to model and interpret the statistical...