Articles | Volume 26, issue 2
Hydrol. Earth Syst. Sci., 26, 221–243, 2022
https://doi.org/10.5194/hess-26-221-2022
Hydrol. Earth Syst. Sci., 26, 221–243, 2022
https://doi.org/10.5194/hess-26-221-2022

Research article 18 Jan 2022

Research article | 18 Jan 2022

Preprocessing approaches in machine-learning-based groundwater potential mapping: an application to the Koulikoro and Bamako regions, Mali

Víctor Gómez-Escalonilla 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-2021-261', Anonymous Referee #1, 05 Aug 2021
    • AC1: 'Reply on RC1', Victor Gómez-Escalonilla, 03 Sep 2021
  • RC2: 'Comment on hess-2021-261', Anonymous Referee #2, 09 Aug 2021
    • AC2: 'Reply on RC2', Victor Gómez-Escalonilla, 03 Sep 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) (21 Sep 2021) by Marnik Vanclooster
AR by Victor Gómez-Escalonilla on behalf of the Authors (04 Oct 2021)  Author's response    Author's tracked changes    Manuscript
ED: Reconsider after major revisions (further review by editor and referees) (18 Oct 2021) by Marnik Vanclooster
ED: Referee Nomination & Report Request started (24 Nov 2021) by Marnik Vanclooster
RR by Fatma Trabelsi (29 Nov 2021)
ED: Publish as is (30 Nov 2021) by Marnik Vanclooster
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Short summary
Many communities in the Sahel rely solely on groundwater. We develop a machine learning technique to map areas of groundwater potential. Algorithms are trained to detect areas where there is a confluence of factors that facilitate groundwater occurrence. Our contribution focuses on using variable scaling to minimize expert bias and on testing our results beyond standard metrics. This approach is illustrated through its application to two administrative regions of Mali.