Articles | Volume 26, issue 2
Hydrol. Earth Syst. Sci., 26, 221–243, 2022
Hydrol. Earth Syst. Sci., 26, 221–243, 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.

Related subject area

Subject: Groundwater hydrology | Techniques and Approaches: Remote Sensing and GIS
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Cited articles

Adeyeye, O. A., Ikpokonte E. A., and Arabi, S. A.: GIS-based groundwater potential mapping within Dengi area, North Central Nigeria, The Egyptian Journal of Remote Sensing and Space Science, 22, 175–181,, 2019. 
Al-Djazouli, M. O., Elmorabiti, K., Rahimi, A., Amellah, O., and Fadil, O. A. M.: Delineating of groundwater potential zones based on remote sensing, GIS and analytical hierarchical process: a case of Waddai, eastern Chad, GeoJournal, 86, 1881–1894,, 2021. 
Al-Fugara, A., Pourghasemi, H. R., Al-Shabeeb, A. R., Habib, M., Al-Adamat, R., Al-Amoush, H., and Collins, A. L.: A comparison of machine learning models for the mapping of groundwater spring potential, Environ. Earth Sci., 79, 206,, 2020. 
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Arneth, A., Barbosa, H., Benton, T., Calvin, K., Calvo, E., Connors, S., Cowie, A., Davin, E., Denton, F., and van Diemen, R.: Summary for policymakers, edited by: Shukla, P. R., Skea, J., Calvo Buendia, E., Masson-Delmotte, V., Pörtner, H.-O., Roberts, D. C., Zhai, P., Slade, R., Connors, S., van Diemen, R., Ferrat, M., Haughey, E., Luz, S., Neogi, S., Pathak, M., Petzold, J., Portugal Pereira, J., Vyas, P., Huntley, E., Kissick, K., Belkacemi, M., and Malley, J., in: Climate Change and Land: an IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems, ISBN 978-92-9169-154-8, Geneva, Switzerland, The Intergovernmental Panel on Climate Change (IPCC), 2019. 
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.