Spatial Prediction of Groundwater Potentiality in Large Semi-Arid and Karstic Mountainous Region Using Machine Learning Models

Abstract

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.

Novel hybrid models to enhance the efficiency of groundwater potentiality model

“…The slope and contributing area are used to determine SPI, which is a measure of the erosive strength of flowing water (Namous et al 2021). The SPI is calculated using Eq.…”

“…The majority of groundwater potentiality-inundation regions are often located around the river's edge. Because river distance effects groundwater potentiality and river flow to river aspect, it is an important factor for finding basin regions with high groundwater potential (Namous et al 2021). The greater the distance between a place and a river, the less probable it is that the area has a big amount of groundwater capacity.…”

“…By integrating RS and GIS, many topographical, hydrological, climatic, pedogenic parameters for extensive areas can be extracted with very high precision (Arabameri et al 2020(Arabameri et al , 2021 (Table 1). Also, these technologies help to assimilate multi-parameters and are able to produce highly accurate groundwater potential maps for large to a small areas within a short time (Dau et al 2021;Namous et al 2021). Therefore, these technologies have added a new dimension to groundwater research (Mallick et al 2021c;Nguyen et al 2020).…”

“…We chose those variables, which many researchers have extensively used. In the plain regions, topographic and climatic parameters have been recognized as important variables, while in the mountains, along with topographic, geological variables have been described as critical variables for GWP mapping (Mallick et al 2021c;Al-Djazouli et al 2021;Pathak et al 2021;Namous et al 2021;Al-Abadi et al 2021). For example, drainage density could be a valid variable in flood plains, not in mountainous regions (Bhattacharya et al 2021;Fadhillah et al 2021).…”

Use of GIS, Remote Sensing and Analytical Hierarchy Process for Groundwater Potential Assessment in an Arid Region – A Case Study

“…Groundwater is an indispensable resource in the socio-economic development of arid regions and the stabilization of their populations. It constitutes the primary source of drinking water supply for its populations as well as the main engine for the evolution of its agricultural activities (Namous et al 2021). However, access to these waters is often difficult, particularly in arid climate regions with crystalline basements, because they are characterized by a complexity of hydrogeological contexts accompanied by a spatiotemporal limitation of precipitation (Yao et al 2016).…”