Articles | Volume 27, issue 22
https://doi.org/10.5194/hess-27-4151-2023
https://doi.org/10.5194/hess-27-4151-2023
Research article
 | 
16 Nov 2023
Research article |  | 16 Nov 2023

Understanding hydrologic controls of sloping soil response to precipitation through machine learning analysis applied to synthetic data

Daniel Camilo Roman Quintero, Pasquale Marino, Giovanni Francesco Santonastaso, and Roberto Greco

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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 egusphere-2022-1078', Anonymous Referee #1, 15 Mar 2023
    • AC1: 'Reply on RC1', Daniel Camilo Roman Quintero, 28 Apr 2023
  • RC2: 'Comment on egusphere-2022-1078', Anonymous Referee #2, 20 Mar 2023
    • AC2: 'Reply on RC2', Daniel Camilo Roman Quintero, 28 Apr 2023
  • RC3: 'Comment on egusphere-2022-1078', Anonymous Referee #3, 27 Mar 2023
    • AC3: 'Reply on RC3', Daniel Camilo Roman Quintero, 28 Apr 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Reconsider after major revisions (further review by editor and referees) (14 May 2023) by Marnik Vanclooster
AR by Daniel Camilo Roman Quintero on behalf of the Authors (23 Jun 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (05 Jul 2023) by Marnik Vanclooster
RR by Anonymous Referee #3 (31 Jul 2023)
RR by Anonymous Referee #1 (02 Aug 2023)
ED: Reconsider after major revisions (further review by editor and referees) (19 Aug 2023) by Marnik Vanclooster
AR by Daniel Camilo Roman Quintero on behalf of the Authors (16 Oct 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (25 Oct 2023) by Marnik Vanclooster
AR by Daniel Camilo Roman Quintero on behalf of the Authors (28 Oct 2023)
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Short summary
This study shows a methodological approach using machine learning techniques to disentangle the relationships among the variables in a synthetic dataset to identify suitable variables that control the hydrologic response of the slopes. It has been found that not only is the rainfall responsible for the water accumulation in the slope; the ground conditions (soil water content and aquifer water level) also indicate the activation of natural slope drainage mechanisms.