The present study was carried out using artificial neural network (ANN) model for predicting the sodium hazardness, i.e., sodium adsorption ratio (SAR), percent sodium (%Na) residual, Kelly’s ratio (KR), and residual sodium carbonate (RSC) in the groundwater of the Pratapgarh district of Southern Rajasthan, India. This study focuses on verifying the suitability of water for irrigational purpose, wherein more groundwater decline coupled with water quality problems compared to the other areas are observed. The southern part of the Rajasthan State is more populated as compared to the rest of the parts. The southern part of the Rajasthan is more populated as compared to the rest of the Rajasthan, which leads to the industrialization, urbanization, and evolutionary changes in the agricultural production in the southern region. Therefore, it is necessary to propose innovative methods for analyzing and predicting the water quality (WQ) for agricultural use. The study aims to develop an optimized artificial neural network (ANN) model to predict the sodium hazardness of groundwater for irrigation purposes. The ANN model was developed using ‘nntool’ in MATLAB software. The ANN model was trained and validated for ten years (2010–2020) of water quality data. An L-M 3-layer back propagation technique was adopted in ANN architecture to develop a reliable and accurate model for predicting the suitability of groundwater for irrigation. Furthermore, statistical performance indicators, such as RMSE, IA, R, and MBE, were used to check the consistency of ANN prediction results. The developed ANN model, i.e., ANN4 (3-12-1), ANN4 (4-15-1), ANN1 (4-5-1), and ANN4 (3-12-1), were found best suited for SAR, %Na, RSC, and KR water quality indicators for the Pratapgarh district. The performance analysis of the developed model (3-12-1) led to a correlation coefficient = 1, IA = 1, RMS = 0.14, and MBE = 0.0050. Hence, the proposed model provides a satisfactory match to the empirically generated datasets in the observed wells. This development of water quality modeling using an ANN model may help to useful for the planning of sustainable management and groundwater resources with crop suitability plans as per water quality.

The Gravity Recovery and Climate Experiment (GRACE) has been successfully used to monitor variations in terrestrial water storage (GRACE_TWS) and groundwater storage (GRACE_GWS) across the globe, yet such applications are hindered on local scales by the limited spatial resolution of GRACE data. Using the Lower Peninsula of Michigan as a test site, we developed optimum procedures to downscale GRACE Release-06 monthly mascon solutions. A four-fold exercise was conducted. Cluster analysis was performed to identify the optimum number and distribution of clusters (areas) of contiguous pixels of similar geophysical signals (GRACE_TWS time series); three clusters were identified (cluster 1: 13,700 km²; cluster 2: 59,200 km²; cluster 3: 33,100 km²; Step I). Variables (total precipitation, normalized difference vegetation index (NDVI), snow cover, streamflow, Lake Michigan level, Lake Huron level, land surface temperature, soil moisture, air temperature, and evapotranspiration (ET)), which could potentially contribute to, or correlate with, GRACE_TWS over the test site were identified, and the dataset was randomly partitioned into training (80%) and testing (20%) datasets (Step II). Multivariate regression, artificial neural network, and extreme gradient boosting techniques were applied on the training dataset for each of the identified clusters to extract relationships between the identified hydro-climatic variables and GRACE_TWS solutions on a coarser scale (13,700–33,100 km²), and were used to estimate GRACE_TWS at a spatial resolution matching that of the fine-scale (0.125° × 0.125° or 120 km²) inputs. The statistical models were evaluated by comparing the observed and modeled GRACE_TWS values using the R-squared, the Nash–Sutcliffe model efficiency coefficient (NSE), and the normalized root-mean-square error (NRMSE; Step III). Lastly, temporal variations in GRACE_GWS were extracted using outputs of land surface models and those of the optimum downscaling methodology (downscaled GRACE_TWS) (Step IV). Findings demonstrate that (1) consideration should be given to the cluster-based extreme gradient boosting technique in downscaling GRACE_TWS for local applications given their apparent enhanced performance (average value: R-squared: 0.86; NRMSE 0.37; NSE 0.86) over the multivariate regression (R-squared: 0.74; NRMSE 0.56; NSE 0.64) and artificial neural network (R-squared: 0.76; NRMSE 0.5; NSE 0.37) methods; and (2) identifying local hydrologic variables and the optimum downscaling approach for individual clusters is critical to implementing this method. The adopted method could potentially be used for groundwater management purposes on local scales in the study area and in similar settings elsewhere.

Thirty groundwater samples were taken from Assiut’s northern outskirts. The physicochemical properties of these samples were investigated. For the evaluation of water–rock interaction, the saturation index (SI), chloro-alkaline indices (CAI1 and CAI2), Gibbs ratios for cations and anions (GC and GA), principal component analysis (PCA), and hierarchical cluster analysis (HCA) were used. (1) With the exception of five samples that were supersaturated, the rest of the groundwater samples were under-saturated with carbonate mineral dissolution (calcite, dolomite, and aragonite) and evaporite mineral dissolution (anhydrite, gypsum, halite, and sylvite). (2) The presence of ion exchange between alkali elements (Na⁺ + K⁺) and earth alkaline elements (Ca²⁺ + Mg²⁺) is explained by the positive values of the chloro-alkaline indices. (3) In four groundwater samples, negative chloro-alkaline indices suggest reverse ion exchange and an increasing alkali element content. (4) The Gibbs diagram demonstrates that, with the exception of four samples that reflect saline groundwater, the majority of the groundwater samples are freshwater recharging from the fresh surface water in the research area. (5) Groundwater types include Ca-HCO₃ (11%), Mg-HCO₃ (48%), Na-HCO₃ (23%), Mg-Cl (13%), and Na-Cl (3%). (6) The alkali, earth alkaline elements, and sulphate are the key controls on groundwater salinity, according to PCA and HCA. (7) The somewhat saline groundwater in the research area’s western desert margins should not be used since it poses a health danger to people.

Recently, machine learning (ML) and deep learning (DL) models based on artificial intelligence (AI) have emerged as fast and reliable tools for predicting water quality index (WQI) in various regions worldwide. In this study, we propose a novel stacking framework based on DL models for WQI prediction, employing a convolutional neural network (CNN) model. Additionally, we introduce explainable AI (XAI) through XGBoost-based SHAP (SHapley Additive exPlanations) values to gain valuable insights that can enhance decision-making strategies in water management. Our findings demonstrate that the stacking model achieves the highest accuracy in WQI prediction (R²: 0.99, MAPE: 15.99%), outperforming the CNN model (R²: 0.90, MAPE: 58.97%). Although the CNN model shows a relatively high R² value, other statistical measures indicate that it is actually the worst-performing model among the five tested. This discrepancy may be attributed to the limited training data available for the CNN model. Furthermore, the application of explainable AI (XAI) techniques, specifically XGBoost-based SHAP values, allows us to gain deep insights into the models and extract valuable information for water management purposes. The SHAP values and interaction plot reveal that elevated levels of total dissolved solids (TDS), zinc, and electrical conductivity (EC) are the primary drivers of poor water quality. These parameters exhibit a nonlinear relationship with the water quality index, implying that even minor increases in their concentrations can significantly impact water quality. Overall, this study presents a comprehensive and integrated approach to water management, emphasizing the need for collaborative efforts among all stakeholders to mitigate pollution levels and uphold water quality. By leveraging AI and XAI, our proposed framework not only provides a powerful tool for accurate WQI prediction but also offers deep insights into the models, enabling informed decision-making in water management strategies.

Human activity has led to a rise in the demand for water, prompting Saudi Arabia to search for alternative groundwater supplies. Wadi As-Sirhan is one area that has experienced extensive agricultural growth and the severe over-exploitation of its groundwater resources. The groundwater drawn from the wadi should be continuously monitored to determine the best management options for groundwater resources and economic growth. The most recent Gravity Recovery and Climate Experiment (GRACE) mission and outputs of land surface models were combined to estimate the depletion rate of the groundwater of the Wadi As-Sirhan drainage basin in the northern region of Saudi Arabia throughout the period of April 2002–December 2021. The findings are: (1) the average GRACE-derived terrestrial water storage variation (ΔTWS) was calculated at −13.82 ± 0.24 mm/yr; (2) the soil moisture storage variation was averaged at +0.008 ± 0.004 mm/yr; (3) the GRACE-derived groundwater depletion rate was estimated at −13.81 ± 0.24 mm/yr; (4) the annual precipitation data over the Wadi As-Sirhan was averaged at 60 mm/yr; (5) The wadi has a minimal recharge rate of +2.31 ± 0.24 mm/yr, which may partially compensate for a portion of the groundwater withdrawal; (6) the sediment thickness shows an increase from 0 m at the southern igneous and volcanic rocks to more than 3000 m close to the Saudi–Jordanian border; (7) The wadi’s eastern, southern, and western portions are the sources of its tributaries, which ultimately drain into its northwestern portion; (8) change detection from the Landsat photos reveals considerable agricultural expansions over recent decades. The integrated method is useful for analyzing changes to groundwater resources in large groundwater reservoirs and developing environmentally appropriate management programs for these resources.

Identifying shallow (near-surface) groundwater in arid and hyper-arid areas has significant societal benefits, yet it is a costly operation when traditional methods (geophysics and drilling) are applied over large domains. In this study, we developed and successfully applied methodologies that rely heavily on readily available temporal, visible, and near-infrared radar and thermal remote sensing data sets and field data, as well as statistical approaches to map the distribution of shallow (1–5 m deep) groundwater occurrences in Al Qunfudah Province, Saudi Arabia, and to identify the factors controlling their development. A four-fold approach was adopted: (1) constructing a digital database to host relevant geologic, hydrogeologic, topographic, land use, climatic, and remote sensing data sets, (2) identifying the distribution of areas characterized by shallow groundwater levels, (3) developing conceptual and statistical models to map the distribution of shallow groundwater occurrences, and (4) constructing an artificial neural network (ANN) and multivariate regression (MR) models to map the distribution of shallow groundwater, test the models over areas of known depth to groundwater (area of Al Qunfudah city and surroundings: 294 km²), and apply the better of the two models to map the shallow groundwater occurrences across the entire Al Qunfudah Province (area: 4680 km²). Findings include: (1) high performance for the ANN (92%) and MR (88%) models in predicting the distribution of shallow groundwater using temporal-derived remote sensing products (e.g., normalized difference vegetation index (NDVI), radar backscatter coefficient, precipitation, and brightness temperature) and field data (depth to water table), (2) areas witnessing shallow groundwater levels show high NDVI (mean and standard deviation (STD)), radar backscatter coefficient values (mean and STD), and low brightness temperature (mean and STD) compared to their surroundings, (3) correlations of temporal groundwater levels and satellite-based precipitation suggest that the observed (2017–2019) rise in groundwater levels is related to an increase in precipitation in these years compared to the previous three years (2014–2016), and (4) the adopted methodologies are reliable, cost-effective, and could potentially be applied to identify shallow groundwater along the Red Sea Hills and in similar settings worldwide.