Articles | Volume 25, issue 11
Hydrol. Earth Syst. Sci., 25, 5917–5935, 2021
https://doi.org/10.5194/hess-25-5917-2021
Hydrol. Earth Syst. Sci., 25, 5917–5935, 2021
https://doi.org/10.5194/hess-25-5917-2021

Research article 15 Nov 2021

Research article | 15 Nov 2021

Evaluating different machine learning methods to simulate runoff from extensive green roofs

Elhadi Mohsen Hassan Abdalla et al.

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-124', Anonymous Referee #1, 20 Apr 2021
    • AC1: 'Reply on RC1', Elhadi Abdalla, 06 Jun 2021
  • RC2: 'Comment on hess-2021-124', Anonymous Referee #2, 14 May 2021
    • AC2: 'Reply on RC2', Elhadi Abdalla, 06 Jun 2021
    • AC3: 'Reply on RC2', Elhadi Abdalla, 06 Jun 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Reconsider after major revisions (further review by editor and referees) (16 Jun 2021) by Christa Kelleher
AR by Elhadi Abdalla on behalf of the Authors (28 Jul 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (10 Aug 2021) by Christa Kelleher
RR by Anonymous Referee #1 (24 Aug 2021)
RR by Anonymous Referee #3 (06 Oct 2021)
ED: Publish subject to minor revisions (review by editor) (06 Oct 2021) by Christa Kelleher
AR by Elhadi Abdalla on behalf of the Authors (08 Oct 2021)  Author's response    Manuscript
ED: Publish as is (18 Oct 2021) by Christa Kelleher
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Short summary
This study investigated the potential of using machine learning algorithms as hydrological models of green roofs across different climatic condition. The study provides comparison between conceptual and machine learning algorithms. Machine learning models were found to be accurate in simulating runoff from extensive green roofs.