Articles | Volume 25, issue 8
Hydrol. Earth Syst. Sci., 25, 4373–4401, 2021
https://doi.org/10.5194/hess-25-4373-2021
Hydrol. Earth Syst. Sci., 25, 4373–4401, 2021
https://doi.org/10.5194/hess-25-4373-2021

Research article 11 Aug 2021

Research article | 11 Aug 2021

Hydrologically informed machine learning for rainfall–runoff modelling: towards distributed modelling

Herath Mudiyanselage Viraj Vidura Herath et al.

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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: Reconsider after major revisions (further review by editor and referees) (01 Jan 2021) by Fabrizio Fenicia
AR by Herath Mudiyanselage Viraj Vidura Herath on behalf of the Authors (10 Jan 2021)  Author's response    Author's tracked changes    Manuscript
ED: Reconsider after major revisions (further review by editor and referees) (14 Jan 2021) by Fabrizio Fenicia
AR by Herath Mudiyanselage Viraj Vidura Herath on behalf of the Authors (06 Feb 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (08 Feb 2021) by Fabrizio Fenicia
RR by Anonymous Referee #3 (30 Mar 2021)
RR by Anonymous Referee #4 (31 Mar 2021)
ED: Reconsider after major revisions (further review by editor and referees) (12 Apr 2021) by Fabrizio Fenicia
AR by Herath Mudiyanselage Viraj Vidura Herath on behalf of the Authors (23 May 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (07 Jun 2021) by Fabrizio Fenicia
RR by Anonymous Referee #4 (02 Jul 2021)
RR by Anonymous Referee #3 (10 Jul 2021)
ED: Publish subject to minor revisions (review by editor) (13 Jul 2021) by Fabrizio Fenicia
AR by Herath Mudiyanselage Viraj Vidura Herath on behalf of the Authors (15 Jul 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (16 Jul 2021) by Fabrizio Fenicia
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Short summary
Existing hydrological knowledge has been integrated with genetic programming based on a machine learning algorithm (MIKA-SHA) to induce readily interpretable distributed rainfall–runoff models. At present, the model building components of two flexible modelling frameworks (FUSE and SUPERFLEX) represent the elements of hydrological knowledge. The proposed toolkit captures spatial variabilities and automatically induces semi-distributed rainfall–runoff models without any explicit user selections.