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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/hess-2020-460
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2020-460
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  07 Oct 2020

07 Oct 2020

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This preprint is currently under review for the journal HESS.

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

Yang Yang and Ting Fong May Chui Yang Yang and Ting Fong May Chui
  • Department of Civil Engineering, The University of Hong Kong, Hong Kong SAR, China

Abstract. Sustainable drainage systems (SuDS) are decentralized stormwater management practices that mimic the natural drainage processes. Their modeling is often challenged by insufficient data and unknown factors affecting the hydrological processes. This study uses machine learning methods to model directly the correlation between hydrological responses and rainfalls at fine temporal scales in two catchments of different sizes. A feature engineering method is developed to extract useful information from rainfall time series and is used in combination with a nested cross-validation procedure to derive high-quality models and to estimate their generalization errors. The SHAP method is adopted to explain the basis of each prediction, which is then used for estimating catchment response time and hydrograph separation. The explanations of the predictions provide valuable insights into the models’ behavior and the involved hydrological processes. Thus, interpreting machine learning models is found as a useful way to study catchment hydrology.

Yang Yang and Ting Fong May Chui

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Yang Yang and Ting Fong May Chui

Yang Yang and Ting Fong May Chui

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Latest update: 26 Oct 2020
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Short summary
This study uses machine learning methods to model the correlation between rainfall time series and outflow rates of urban catchments with sustainable urban drainage systems. The models have good prediction accuracy, and the contribution of rainfall at each time step to runoffs at different steps is identified. The models offer plausible conceptualizations of physical processes, which are useful for various tasks, such as baseflow separation and catchment response time estimation.
This study uses machine learning methods to model the correlation between rainfall time series...
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