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

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to revisions (further review by editor and referees) (08 Jan 2021) by Roberto Greco
AR by Yang Yang on behalf of the Authors (03 Apr 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (06 Apr 2021) by Roberto Greco
RR by Anonymous Referee #3 (08 Jul 2021)
ED: Reconsider after major revisions (further review by editor and referees) (09 Jul 2021) by Roberto Greco
AR by Yang Yang on behalf of the Authors (04 Sep 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (06 Sep 2021) by Roberto Greco
RR by Anonymous Referee #3 (27 Sep 2021)
ED: Publish as is (07 Oct 2021) by Roberto Greco
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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.