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.
Research article
| 
18 Jan 2022
Research article |
| 18 Jan 2022
| 18 Jan 2022
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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- Spatial prediction of groundwater potential by various novel boosting-based ensemble learning models in mountainous areas H. Xiong et al. 10.1080/10106049.2023.2274870
- Integrating geological, hydrogeological and geophysical data to identify groundwater resources in granitic basement areas (Guéra Massif, Chad) H. Nouradine et al. 10.1007/s10040-024-02766-2
- Mapping groundwater potentiality by using hybrid machine learning models under the scenario of climate variability: a national level study of Bangladesh S. Sarkar et al. 10.1007/s10668-024-04687-2
- Assessment of potential health risks from heavy metal pollution of surface water for drinking in a multi-industry area in Mali using a multi-indices approach L. Sangaré et al. 10.1007/s10661-023-11258-x
- Determination of potential recharge zones and its validation against groundwater quality parameters through the application of GIS and remote sensing techniques in uMhlathuze catchment, KwaZulu-Natal, South Africa D. Ponnusamy & V. Elumalai 10.1016/j.chemosphere.2022.136121
- Integrated machine learning and remote sensing for groundwater potential mapping in the Mekong Delta in Vietnam H. Nguyen et al. 10.1007/s11600-024-01331-5
- Spatial Prediction of Groundwater Withdrawal Potential Using Shallow, Hybrid, and Deep Learning Algorithms in the Toudgha Oasis, Southeast Morocco L. Ouali et al. 10.3390/su15053874
- MaxEnt machine learning model predicts high groundwater potential areas in a fractured volcanic aquifer system S. Ballardin et al. 10.1016/j.jsames.2024.104794
- Predicting groundwater potential assessment in water-deficient islands based on convolutional neural networks H. Xu et al. 10.1016/j.ejrs.2022.11.002
- Identification of groundwater potential zones in data-scarce mountainous region using explainable machine learning K. Dahal et al. 10.1016/j.jhydrol.2023.130417
- Developing meta-heuristic optimization based ensemble machine learning algorithms for hydraulic efficiency assessment of storm water grate inlets Ö. Ekmekcioğlu et al. 10.1080/1573062X.2022.2134806
- Delineating groundwater potential zones using geospatial techniques and fuzzy analytical hierarchy process (FAHP) ensemble in the data-scarce region: evidence from the lower Thoubal river watershed of Manipur, India M. Rahaman et al. 10.1007/s12517-022-09946-y
- Groundwater potential zoning by integrating multi-criteria decision and bivariate analysis methods – a case study on Cheyyar River Basin, Tamil Nadu, India V. Narayanamurthi & A. Ramasamy 10.1080/10106049.2022.2088864
- Identification of non-conventional groundwater resources by means of machine learning in the Aconcagua basin, Chile M. Aliaga-Alvarado et al. 10.1016/j.ejrh.2023.101502
- Potential of machine learning algorithms in groundwater level prediction using temporal gravity data H. Sarkar et al. 10.1016/j.gsd.2024.101114
- A comparative and coupled study of the application of Dempster-Shafer, fuzzy overlay and FAHP methods for groundwater potential mapping in a fractured medium of a mine site M. Safari et al. 10.1007/s12145-023-01006-7
- Multiclass spatial predictions of borehole yield in southern Mali by means of machine learning classifiers . Gómez-Escalonilla et al. 10.1016/j.ejrh.2022.101245
- Critical role of climate factors for groundwater potential mapping in arid regions: Insights from random forest, XGBoost, and LightGBM algorithms X. Guo et al. 10.1016/j.jhydrol.2023.129599
- Application of advanced machine learning algorithms and geospatial techniques for groundwater potential zone mapping in Gambela Plain, Ethiopia T. Seifu et al. 10.2166/nh.2023.083
- Sustainable groundwater development using semi-supervised learning and community-led total forestry and pasture approach U. S et al. 10.1016/j.gsd.2024.101093
- Delineation of groundwater potential zones by means of ensemble tree supervised classification methods in the Eastern Lake Chad basin G. Víctor et al. 10.1080/10106049.2021.2007298
22 citations as recorded by crossref.
- Developing a new method for future groundwater potentiality mapping under climate change in Bisha watershed, Saudi Arabia J. Mallick et al. 10.1080/10106049.2022.2088861
- Ensemble Machine Learning Techniques for Accurate and Efficient Detection of Botnet Attacks in Connected Computers S. Afrifa et al. 10.3390/eng4010039
- Spatial prediction of groundwater potential by various novel boosting-based ensemble learning models in mountainous areas H. Xiong et al. 10.1080/10106049.2023.2274870
- Integrating geological, hydrogeological and geophysical data to identify groundwater resources in granitic basement areas (Guéra Massif, Chad) H. Nouradine et al. 10.1007/s10040-024-02766-2
- Mapping groundwater potentiality by using hybrid machine learning models under the scenario of climate variability: a national level study of Bangladesh S. Sarkar et al. 10.1007/s10668-024-04687-2
- Assessment of potential health risks from heavy metal pollution of surface water for drinking in a multi-industry area in Mali using a multi-indices approach L. Sangaré et al. 10.1007/s10661-023-11258-x
- Determination of potential recharge zones and its validation against groundwater quality parameters through the application of GIS and remote sensing techniques in uMhlathuze catchment, KwaZulu-Natal, South Africa D. Ponnusamy & V. Elumalai 10.1016/j.chemosphere.2022.136121
- Integrated machine learning and remote sensing for groundwater potential mapping in the Mekong Delta in Vietnam H. Nguyen et al. 10.1007/s11600-024-01331-5
- Spatial Prediction of Groundwater Withdrawal Potential Using Shallow, Hybrid, and Deep Learning Algorithms in the Toudgha Oasis, Southeast Morocco L. Ouali et al. 10.3390/su15053874
- MaxEnt machine learning model predicts high groundwater potential areas in a fractured volcanic aquifer system S. Ballardin et al. 10.1016/j.jsames.2024.104794
- Predicting groundwater potential assessment in water-deficient islands based on convolutional neural networks H. Xu et al. 10.1016/j.ejrs.2022.11.002
- Identification of groundwater potential zones in data-scarce mountainous region using explainable machine learning K. Dahal et al. 10.1016/j.jhydrol.2023.130417
- Developing meta-heuristic optimization based ensemble machine learning algorithms for hydraulic efficiency assessment of storm water grate inlets Ö. Ekmekcioğlu et al. 10.1080/1573062X.2022.2134806
- Delineating groundwater potential zones using geospatial techniques and fuzzy analytical hierarchy process (FAHP) ensemble in the data-scarce region: evidence from the lower Thoubal river watershed of Manipur, India M. Rahaman et al. 10.1007/s12517-022-09946-y
- Groundwater potential zoning by integrating multi-criteria decision and bivariate analysis methods – a case study on Cheyyar River Basin, Tamil Nadu, India V. Narayanamurthi & A. Ramasamy 10.1080/10106049.2022.2088864
- Identification of non-conventional groundwater resources by means of machine learning in the Aconcagua basin, Chile M. Aliaga-Alvarado et al. 10.1016/j.ejrh.2023.101502
- Potential of machine learning algorithms in groundwater level prediction using temporal gravity data H. Sarkar et al. 10.1016/j.gsd.2024.101114
- A comparative and coupled study of the application of Dempster-Shafer, fuzzy overlay and FAHP methods for groundwater potential mapping in a fractured medium of a mine site M. Safari et al. 10.1007/s12145-023-01006-7
- Multiclass spatial predictions of borehole yield in southern Mali by means of machine learning classifiers . Gómez-Escalonilla et al. 10.1016/j.ejrh.2022.101245
- Critical role of climate factors for groundwater potential mapping in arid regions: Insights from random forest, XGBoost, and LightGBM algorithms X. Guo et al. 10.1016/j.jhydrol.2023.129599
- Application of advanced machine learning algorithms and geospatial techniques for groundwater potential zone mapping in Gambela Plain, Ethiopia T. Seifu et al. 10.2166/nh.2023.083
- Sustainable groundwater development using semi-supervised learning and community-led total forestry and pasture approach U. S et al. 10.1016/j.gsd.2024.101093
Latest update: 26 Apr 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...
