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.

Viewed

Total article views: 2,017 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,486 488 43 2,017 17 16
  • HTML: 1,486
  • PDF: 488
  • XML: 43
  • Total: 2,017
  • BibTeX: 17
  • EndNote: 16
Views and downloads (calculated since 30 Jun 2021)
Cumulative views and downloads (calculated since 30 Jun 2021)

Viewed (geographical distribution)

Total article views: 2,017 (including HTML, PDF, and XML) Thereof 1,835 with geography defined and 182 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 08 Dec 2022
Download
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.