Saudi Arabia’s arid and semi-arid regions suffer from water scarcity because of climatic constraints and rapid growth of domestic and industrial water uses. The growing demand for high-quality water supplies and to reduce the dependency on desalination creates an urgent need to explore groundwater resources as an alternative. The weighted overlay analysis method using the fuzzy-analytical hierarchy process (FAHP) multi-criteria decision making (MCDM) techniques combined with geoinformation technology was used in this study to explore the groundwater potential zones in the Itwad-Khamis watershed of Saudi Arabia. Twelve thematic layers were prepared and processed in a GIS setting to produce the groundwater potential zone map (GPZM). Subsequently, potential groundwater areas were delineated and drawn into five classes: very good potential, good potential, moderate potential, poor potential, and very poor potential. The estimated GWPZ (groundwater potential zones) was validated by analyzing the existing open wells distribution and the yield data of selected wells within the studied watershed. With this quality-based zoning, it was found that 82% of existing wells were located in a very good and good potential area. The statistical analysis showed that 14.6% and 28.8% of the total area were under very good and good, while 27.3% and 20.2% were accounted for the moderate and poor potential zone, respectively. To achieve sustainable groundwater management in the Aseer region, Saudi Arabia, this research provided a primary estimate and significant insights for local water managers and authorities by providing groundwater potential zone map.

Saudi Arabia is an arid country with very limited water resources. The absence of surface water bodies along with erratic rainfall renders groundwater as the most reliable source of potable water in arid and semi-arid regions globally. Groundwater quality is determined by aquifer characteristics regional geology and it is extensively influenced by both natural and anthropogenic activities. In the recent past, several methodologies have been adopted to analyze the quality of groundwater and associated hydro-geochemical process i.e., multivariate statistical analysis, geochemical modelling, stable isotopes, a redox indicator, structural equation modelling. In the current study, statistical methods combined with geochemical modelling and conventional plots have been used to investigate groundwater and related geochemical processes in the Aseer region of Saudi Arabia. A total of 62 groundwater samples has been collected and analyzed in laboratory for major cations and anions. Groundwater in the study region is mostly alkaline with electrical conductivity ranging from 285–3796 μS/cm. The hydro-geochemical characteristics of groundwater are highly influenced by extreme evaporation. Climatic conditions combined with low rainfall and high temperature have resulted in a highly alkaline aquifer environment. Principal component analysis (PCA) yielded principal components explaining 79.9% of the variance in the dataset. PCA indicates ion exchange, soil mineralization, dissolution of carbonates and halite are the major processes governing the groundwater geochemistry. Groundwater in this region is oversaturated with calcite and dolomite while undersaturated with gypsum and halite which suggests dissolution of gypsum and halite as major process resulting into high chloride in groundwater. The study concludes that the combined approach of a multivariate statistical technique, conventional plots and geochemical modelling is effective in determining the factors controlling the groundwater quality.

Disastrous natural hazards, such as landslides, floods, and forest fires cause a serious threat to natural resources, assets and human lives. Consequently, landslide risk assessment has become requisite for managing the resources in future. This study was designed to develop four ensemble metaheuristic machine learning algorithms, such as grey wolf optimized based artificial neural network (GW-ANN), grey wolf optimized based random forest (GW-RF), particle swarm optimization optimized based ANN (PSO-ANN), and PSO optimized based RF for modeling rainfall-induced landslide susceptibility (LS) in Aqabat Al-Sulbat, Asir region, Saudi Arabia, which observes landslide frequently. To obtain very high precision and robust prediction from machine learning algorithms, the grey wolf and PSO optimization algorithms were integrated to develop new ensemble machine learning techniques. Subsequently, LS maps produced by training dataset were validated using the receiver operating characteristics (ROC) curve based on the testing dataset. Based on the area under curve (AUC) value of ROC curve, the best method for LS modeling was selected. We developed ROC curve-based sensitivity analysis to investigate the influence of the parameters for LS modeling. The Gumble extreme value distribution was employed to estimate the rainfall at 2, 5, 10, 20, 50, and 100 year return periods. Then, the landslide hazard maps were prepared at different return periods by integrating the best LS model and estimated rainfall at different return periods. The theory of danger pixels was employed to prepare a final risk assessment of the resources, which have been exposed to the landslide. The results showed that 27–42 and 6–15 km² were predicted as the very high and high LS zones using four ensemble metaheuristic machine learning algorithms. Based on the area under curve (AUC) of ROC, GR-ANN (AUC-0.905) appeared as the best model for LS modeling. The areas under high and very high landslide hazard were gradually increased over the progression of time (26 km² at the 2 year return period and 40 km² at the 100 year return period for the high landslide hazard zone, and 6 km² at the 2 year return period and 20 km² at the 100 year return period for the very high landslide hazard zone). Similarly, the areas of danger pixel also increased gradually from the 2 to 100 year return periods (37 km² to 62 km²). Various natural resources, such as scrubland, built up, and sparse vegetation, were identified under risk zone due to landslide hazards. In addition, these resources would be exposed extensively to landslides over the advancement of return periods. Therefore, the outcome of the present study will help planners and scientists to propose high precision management plans for protecting natural resources, which have been exposed to landslides.