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.

Data sets

MLMapper P. Martínez-Santos and P. Renard https://www.ucm.es/hidrogeologia/programas-software

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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.