Spatial prediction of groundwater potentiality in large semi‐arid and karstic mountainous region using machine learning models
Namous, M
Hssaisoune, M
Pradhan, B
Lee, CW
Alamri, A
Elaloui, A
Edahbi, M
Krimissa, S
Eloudi, H
Ouayah, M
Elhimer, H
Tagma, T
Lee, CW
Alamri, A
Elaloui, A
Edahbi, M
Krimissa, S
Eloudi, H
Ouayah, M
Elhimer, H
Tagma, T
- Publisher:
- MDPI AG
- Publication Type:
- Journal Article
- Citation:
- Water: an open access journal, 2021, 13, (16), pp. 1-34
- Issue Date:
- 2021-08-02
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The drinking and irrigation water scarcity is a major global issue, particularly in arid and semi‐arid zones. In rural areas, groundwater could be used as an alternative and additional water supply source in order to reduce human suffering in terms of water scarcity. In this context, the purpose of the present study is to facilitate groundwater potentiality mapping via spatial‐modelling techniques, individual and ensemble machine‐learning models. Random forest (RF), logistic regression (LR), decision tree (DT) and artificial neural networks (ANNs) are the main algorithms used in this study. The preparation of groundwater potentiality maps was assembled into 11 ensembles of models. Overall, about 374 groundwater springs was identified and inventoried in the mountain area. The spring inventory data was randomly divided into training (75%) and testing (25%) datasets. Twenty‐four groundwater influencing factors (GIFs) were selected based on a multicollinearity test and the information gain calculation. The results of the groundwater potentiality mapping were validated using statistical measures and the receiver operating characteristic curve (ROC) method. Finally, a ranking of the 15 models was achieved with the prioritization rank method using the compound factor (CF) method. The ensembles of models are the most stable and suitable for groundwater potentiality mapping in mountainous aquifers compared to individual models based on success and prediction rate. The most efficient model using the area under the curve validation method is the RF‐LR‐DT‐ANN ensemble of models. Moreover, the results of the prioritization rank indicate that the best models are the RF‐DT and RF‐LR‐DT ensembles of models.
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