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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This study focuses on the morphotectonics of the Central High Atlas in Morocco through analysis of morphotectonic indices recorded by topography, drainage networks, and longitudinal stream profiles. The methods used in this work were stream lengthgradient index (SLI), normalized steepness index, area-altitude relations (hypsometric curves), mountain front sinuosity, drainage basin shape ratio, and asymmetry factor. The aim is to identify the role of recent tectonic activity resulting in the uplift of the Atlas chain in general and the Central High Atlas in particular. The analysis of the main rivers in the study area using linear geomorphic proxies revealed the presence of several knickpoints (about 28) most likely related to recent tectonic activity; further, the spatial distribution of SLI values reveals the presence of many anomalous zones, which are distinguished by elevated values that are perfectly aligned with major faults. The calculation of areal geomorphic proxies supports and confirms these findings. The results obtained show that the study area has been influenced by recent tectonics, and that some areas of the Central High Atlas have experienced a recent uplift due probably to a reactivation of several thrust and oblique-slip faults, then the high topography can be explained by a compressive component of the transpressional geodynamic regime suggested in the Plio-Quaternary period.