Articles | Volume 26, issue 2
https://doi.org/10.5194/hess-26-221-2022
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, Pedro Martínez-Santos, and Miguel Martín-Loeches

Related subject area

Subject: Groundwater hydrology | Techniques and Approaches: Remote Sensing and GIS
Influence of intensive agriculture and geological heterogeneity on the recharge of an arid aquifer system (Saq–Ram, Arabian Peninsula) inferred from GRACE data
Pierre Seraphin, Julio Gonçalvès, Bruno Hamelin, Thomas Stieglitz, and Pierre Deschamps
Hydrol. Earth Syst. Sci., 26, 5757–5771, https://doi.org/10.5194/hess-26-5757-2022,https://doi.org/10.5194/hess-26-5757-2022, 2022
Short summary
Evaluating downscaling methods of GRACE (Gravity Recovery and Climate Experiment) data: a case study over a fractured crystalline aquifer in southern India
Claire Pascal, Sylvain Ferrant, Adrien Selles, Jean-Christophe Maréchal, Abhilash Paswan, and Olivier Merlin
Hydrol. Earth Syst. Sci., 26, 4169–4186, https://doi.org/10.5194/hess-26-4169-2022,https://doi.org/10.5194/hess-26-4169-2022, 2022
Short summary
Applicability of Landsat 8 thermal infrared sensor for identifying submarine groundwater discharge springs in the Mediterranean Sea basin
Sònia Jou-Claus, Albert Folch, and Jordi Garcia-Orellana
Hydrol. Earth Syst. Sci., 25, 4789–4805, https://doi.org/10.5194/hess-25-4789-2021,https://doi.org/10.5194/hess-25-4789-2021, 2021
Short summary
Unsaturated zone model complexity for the assimilation of evapotranspiration rates in groundwater modelling
Simone Gelsinari, Valentijn R. N. Pauwels, Edoardo Daly, Jos van Dam, Remko Uijlenhoet, Nicholas Fewster-Young, and Rebecca Doble
Hydrol. Earth Syst. Sci., 25, 2261–2277, https://doi.org/10.5194/hess-25-2261-2021,https://doi.org/10.5194/hess-25-2261-2021, 2021
Short summary
Technical note: Water table mapping accounting for river–aquifer connectivity and human pressure
Mathias Maillot, Nicolas Flipo, Agnès Rivière, Nicolas Desassis, Didier Renard, Patrick Goblet, and Marc Vincent
Hydrol. Earth Syst. Sci., 23, 4835–4849, https://doi.org/10.5194/hess-23-4835-2019,https://doi.org/10.5194/hess-23-4835-2019, 2019

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, https://doi.org/10.1016/j.ejrs.2018.04.003, 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, https://doi.org/10.1007/s10708-020-10160-0, 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, https://doi.org/10.1007/s12665-020-08944-1, 2020. 
Angelis, L. and Stamelos, I.: A simulation tool for efficient analogy based cost estimation, Empir. Softw. Eng., 5, 35–68, https://doi.org/10.1023/A:1009897800559, 2000. 
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. 
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.