A Multidisciplinary Approach for Groundwater Potential Mapping in a Fractured Semi-Arid Terrain (Kerdous Inlier, Western Anti-Atlas, Morocco)

Groundwater salinity modeling and mapping using machine learning approaches: a case study in Sidi Okba region, Algeria

The groundwater salinization process complexity and the lack of data on its controlling factors are the main challenges for accurate predictions and mapping of aquifer salinity. For this purpose, effective machine learning (ML) methodologies are employed for effective modeling and mapping of groundwater salinity (GWS) in the Mio-Pliocene aquifer in the Sidi Okba region, Algeria, based on limited dataset of electrical conductivity (EC) measurements and readily available digital elevation model (DEM) derivatives. The dataset was randomly split into training (70%) and testing (30%) sets, and three wrapper selection methods, recursive feature elimination (RFE), forward feature selection (FFS), and backward feature selection (BFS) are applied to train the data. The resulting combinations are used as inputs for five ML models, namely random forest (RF), hybrid neuro-fuzzy inference system (HyFIS), K-nearest neighbors (KNN), cubist regression model (CRM), and support vector machine (SVM). The best-performing model is identified and applied to predict and map GWS across the entire study area. It is highlighted that the applied methods yield input variation combinations as critical factors that are often overlocked by many researchers, which substantially impacts the models' accuracy. Among different alternatives the RF model emerged as the most effective for predicting and mapping GWS in the study area, which led to the high performance in both the training (RMSE = 1.016, R = 0.854, and MAE = 0.759) and testing (RMSE = 1.069, R = 0.831, and MAE = 0.921) phases. The generated digital map highlighted the alarming situation regarding excessive GWS levels in the study area, particularly in zones of low elevations and far from the Foum Elgherza dam and Elbiraz wadi. Overall, this study represents a significant advancement over previous approaches, offering enhanced predictive performance for GWS with the minimum number of input variables.

Application of Analytical Hierarchy Process and Geophysical Method for Groundwater Potential Mapping in the Tata Basin, Morocco

Ensuring water availability for agriculture and drinking water supply in semi-arid mountainous regions requires control of factors influencing groundwater availability. In most cases, the population draws its water needs from the alluvial aquifers close to villages that are already limited and influenced by current climatic change. In addition, the establishment of deep wells in the hard rock aquifers depletes the aquifer. Hence, understanding the factors influencing water availability is an urgent requirement. The use of geographic information system (GIS), and remote sensing (RS), together with decision-making methods like analytical hierarchy process (AHP) will be of good aid in this regard. In the Tata basin, located in SE Morocco, ten factors were used to explain the groundwater potentiality map (GWPM). Five categories of potential zones were determined: very low (8.67%), low (17.74%), moderate (46.77%), high (19.95%), and very high (6.87%). The efficiency of the AHP model is validated using the ROC curve (receiver operating characteristics) which revealed a good correlation between the high potential groundwater zones and the spatial distribution of high flow wells. Geophysical prospecting, using electrical resistivity profiles, has made it possible to propose new well sites. It corresponds to conductive resistivity zones that coincide with the intersection of hydrogeological lineaments.