Articles | Volume 26, issue 2
https://doi.org/10.5194/hess-26-221-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-221-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Preprocessing approaches in machine-learning-based groundwater potential mapping: an application to the Koulikoro and Bamako regions, Mali
UNESCO/UNITWIN Chair Appropriate Technologies for Human Development, Department of Geodynamic, Stratigraphy and Paleontology, Faculty of Geology,
Complutense University of Madrid, 28040
Madrid, Spain
Pedro Martínez-Santos
UNESCO/UNITWIN Chair Appropriate Technologies for Human Development, Department of Geodynamic, Stratigraphy and Paleontology, Faculty of Geology,
Complutense University of Madrid, 28040
Madrid, Spain
Miguel Martín-Loeches
Department of Geology, Geography and Environmental Science,
University of Alcalá, Alcalá de Henares, Madrid, Spain
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Latest update: 04 Nov 2024
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.
Many communities in the Sahel rely solely on groundwater. We develop a machine learning...