A Commune-Level Groundwater Potential Map for the Republic of Mali

Díaz-Alcaide, S.; Martínez‐Santos, Pedro; Villarroya, Fermín

doi:10.3390/w9110839

Cited by 13 publications

(10 citation statements)

References 18 publications

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“…GPM is desirable, since surface water sources are limited. Furthermore, surface water is prone to seasonal fluctuations and can succumb to pollution due to anthropogenic activities [7,8]. In addition, surface water requires physical harvesting structures, such as reservoirs and dams, which are costly and require specific technical skills to operate.…”

Section: Introductionmentioning

confidence: 99%

Optimized Conditioning Factors Using Machine Learning Techniques for Groundwater Potential Mapping

Kalantar

Al-Najjar

Pradhan

et al. 2019

Water

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Assessment of the most appropriate groundwater conditioning factors (GCFs) is essential when performing analyses for groundwater potential mapping. For this reason, in this work, we look at three statistical factor analysis methods-Variance Inflation Factor (VIF), Chi-Square Factor Optimization, and Gini Importance-to measure the significance of GCFs. From a total of 15 frequently used GCFs, 11 most effective ones (i.e., altitude, slope angle, plan curvature, profile curvature, topographic wetness index, distance from river, distance from fault, river density, fault density, land use, and lithology) were finally selected. In addition, 917 spring locations were identified and used to train and test three machine learning algorithms, namely Mixture Discriminant Analysis (MDA), Linear Discriminant Analysis (LDA) and Random Forest (RF). The resultant trained models were then applied for groundwater potential prediction and mapping in the Haraz basin of Mazandaran province, Iran. MDA has been successfully applied for soil erosion and landslide mapping, but has not yet been fully explored for groundwater potential mapping (GPM). Although other discriminant methods, such as LDA, exist, MDA is worth exploring due to its capability to model multivariate nonlinear relationships between variables; it also undertakes a mixture of unobserved subclasses with regularization of non-linear decision boundaries, which could potentially provide more accurate classification. For the validation, areas under Receiver Operating Characteristics (ROC) curves (AUC) were calculated for the three algorithms. RF performed better with AUC value of 84.4%, while MDA and LDA yielded 75.2% and 74.9%, respectively. Although MDA performance is lower than RF, the result is satisfactory, because it is within the acceptable standard of environmental modeling. The outcome of factor analysis and groundwater maps emphasizes on optimization of multicolinearity factors for faster spatial modeling and provides valuable information for government agencies and private sectors to effectively manage groundwater in the region.

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Section: Introductionmentioning

confidence: 99%

Optimized Conditioning Factors Using Machine Learning Techniques for Groundwater Potential Mapping

Kalantar

Al-Najjar

Pradhan

et al. 2019

Water

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“…Today, 2.5 billion people across the world depend on groundwater for domestic supply (Grönwall and Danert, 2020). Groundwater is crucial in arid regions, such as the Sahel, where aquifers provide a permanent source of good-quality water during the months of the year in which rainfall and surface water are absent (Llamas and Martínez-Santos, 2005;Díaz-Alcaide et al, 2017). In a context of climate change, with rainfall expected to decrease and droughts likely to become more intense (Arneth et al, 2019), groundwater resources will be increasingly relied upon.…”

Section: Introductionmentioning

confidence: 99%

“…Recent years have witnessed a growing interest in groundwater potential studies across Africa, largely as a result of the need to achieve Sustainable Development Goal 6. The majority of these studies rely on a combination of remote sensing, geographic information systems and geophysics (Delgado, 2018;Adeyeye et al, 2019;Magaia et al, 2018;Mpofu et al, 2020;Owolabi et al, 2020;Saadi et al, 2021;Al-Djazouli et al, 2021), whereas others directly interpret the information from borehole databases (Díaz-Alcaide et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Preprocessing approaches in machine-learning-based groundwater potential mapping: an application to the Koulikoro and Bamako regions, Mali

Gómez-Escalonilla¹,

Martínez‐Santos

Martín-Loeches

2022

Hydrol. Earth Syst. Sci.

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Abstract. Groundwater is crucial for domestic supplies in the Sahel, where the strategic importance of aquifers will increase in the coming years due to climate change. Groundwater potential mapping is a valuable tool to underpin water management in the region and, hence, to improve drinking water access. This paper presents a machine learning method to map groundwater potential. This is illustrated through its application in two administrative regions of Mali. A set of explanatory variables for the presence of groundwater is developed first. Scaling methods (standardization, normalization, maximum absolute value and max–min scaling) are used to avoid the pitfalls associated with reclassification. Noisy, collinear and counterproductive variables are identified and excluded from the input dataset. A total of 20 machine learning classifiers are then trained and tested on a large borehole database (n=3345) in order to find meaningful correlations between the presence or absence of groundwater and the explanatory variables. Maximum absolute value and standardization proved the most efficient scaling techniques, while tree-based algorithms (accuracy >0.85) consistently outperformed other classifiers. The borehole flow rate data were then used to calibrate the results beyond standard machine learning metrics, thereby adding robustness to the predictions. The southern part of the study area presents the better groundwater prospect, which is consistent with the geological and climatic setting. Outcomes lead to three major conclusions: (1) picking the best performers out of a large number of machine learning classifiers is recommended as a good methodological practice, (2) standard machine learning metrics should be complemented with additional hydrogeological indicators whenever possible and (3) variable scaling contributes to minimize expert bias.

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“…Today, 2.5 billion people depend exclusively on groundwater for their domestic supply (Grönwall and Danert, 2020). Groundwater is particularly crucial in most of the Sahel, where rainfall and surface water are absent for several months (Llamas and Martínez-Santos, 2005;Díaz-Alcaide et al, 2017). In a context of climate change, where rainfall will decrease in most arid and semiarid regions and drought episodes will intensify (Arneth et al, 2019), groundwater resources are expected to be increasingly relied upon.…”

Section: Introductionmentioning

confidence: 99%

Preprocessing approaches in machine learning-based groundwater potential mapping: an application to the Koulikoro and Bamako regions, Mali

Gómez-Escalonilla

Martínez‐Santos

Martín-Loeches

2021

Preprint

Self Cite

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Abstract. Groundwater is crucial for domestic supplies in the Sahel, where the strategic importance of aquifers can only be expected to increase in the coming years due to climate change. Groundwater potential mapping is gaining recognition as a valuable tool to underpin water management practices in the region, and hence, to improve water access. This paper presents a machine learning method to map groundwater potential and illustrates it through an application to two regions of Mali. A set of explanatory variables for the presence of groundwater is developed first. Several scaling methods (standardization, normalization, maximum absolute value and min-max scaling) are used to avoid the pitfalls associated with the reclassification of explanatory variables. A number of supervised learning classifiers is then trained and tested on a large borehole database (n = 3,345) in order to find meaningful correlations between the presence or absence of groundwater and the explanatory variables. This process identifies noisy, collinear and counterproductive variables and excludes them from the input dataset. Tree-based algorithms, including the AdaBoost, Gradient Boosting, Random Forest, Decision Tree and Extra Trees classifiers were found to outperform other algorithms on a consistent basis (accuracy > 0.85), whereas maximum absolute value and standardization proved the most efficient methods to scale explanatory variables. Borehole flow rate data is used to calibrate the results beyond standard machine learning metrics, thus adding robustness to the predictions. The southern part of the study area was identified as the better groundwater prospect, which is consistent with the geological and climatic setting. From a methodological standpoint, the outcomes lead to three major conclusions: (1) because there is no aprioristic way to know which algorithm will work better on a given dataset, we advocate the use of a large number of machine learning classifiers, out of which the best are subsequently picked for ensembling; (2) standard machine learning metrics may be of limited value when appraising map outcomes, and should be complemented with hydrogeological indicators whenever possible; and (3) the scaling of the variables helps to minimize bias arising from expert judgement and maintains robust predictive capabilities.

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A Commune-Level Groundwater Potential Map for the Republic of Mali

Cited by 13 publications

References 18 publications

Optimized Conditioning Factors Using Machine Learning Techniques for Groundwater Potential Mapping

Optimized Conditioning Factors Using Machine Learning Techniques for Groundwater Potential Mapping

Preprocessing approaches in machine-learning-based groundwater potential mapping: an application to the Koulikoro and Bamako regions, Mali

Preprocessing approaches in machine learning-based groundwater potential mapping: an application to the Koulikoro and Bamako regions, Mali

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