Groundwater has recently been considered one of the primary sources of water supply in Sudan. However, groundwater quality is continuously degraded due to overexploitation and long-term agricultural operations. The fossilized Cretaceous Nubian sandstone is the principal aquifer in the study area. This research aims to determine the major factors influencing groundwater quality and detect the suitability of groundwater for drinking and irrigation purposes by integrating hydrochemical and multivariate statistical methods. Hydrochemical plots such as Piper, Chadha, and Durov diagrams were applied to detect the groundwater facies and hydrochemical processes controlling the groundwater quality. They indicated Ca–Mg–HCO₃ water type as a dominant groundwater facies followed by Na–HCO₃ and Na–Cl types. Gibbs plots suggested that the dissolution of the minerals is the main factor influencing the water quality. The results of the Gibbs plot were further interpreted using saturation indices (SI). The SI values indicated that aragonite, calcite, and dolomite precipitated respectively in 58.33%, 75%, and 75% of groundwater samples. Multivariate statistical analyses, including Pearson's correlation analysis, hierarchical cluster analysis (HCA), and principal component analyses (PCA), were jointly employed to identify the structure of water quality data and deduce the main factors controlling groundwater quality. The statistical analysis revealed the effect of the physical and human-induced activities as the main factors influencing groundwater chemistry. These factors are rock-water interaction, agricultural practice, and organic contamination from septic tanks. Further, the suitability of groundwater for irrigation is determined using sodium adsorption ratio (SAR) and sodium percent (Na⁺%) indices. They carefully indicated that 75% of the groundwater samples in the study area are excellent for irrigation except for some sample location where the salinity hazard is stimulated by ion exchange. This integrated approach was effective in calibrating water quality assessment methodologies. The current research concluded that the implication of a groundwater quality monitoring scheme is crucial to ensure water supply sustainability in north Bahri city.

In the present study, multilayer perceptron (MLP) neural network and support vector regression (SVR) models were developed to assess the suitability of groundwater for drinking purposes in the northern Khartoum area, Sudan. The groundwater quality was evaluated by predicting the groundwater quality index (GWQI). GWQI is a statistical model that uses sub-indices and accumulation functions to reduce the dimensionality of groundwater quality data. In the first stage, GWQI was calculated using 11 physiochemical parameters collected from 20 groundwater wells. These parameters include pH, EC, TDS, TH, Cl⁻, SO₄⁻², NO₃⁻, Ca⁺², Mg⁺², Na⁺, and HCO₃⁻. The primary investigation confirmed that all parameters except for EC and NO₃⁻ are beyond the standard limits of the World Health Organization (WHO). The measured GWQI ranged from 21 to 396. As a result, groundwater samples were classified into three classes. The majority of the samples, roughly 75%, projected into the excellent water category; 20% were considered good water and 5% were classified as unsuitable. GWQI models are powerful tools in groundwater quality assessment; however, the computation is lengthy, time-consuming, and often associated with calculation errors. To overcome these limitations, this study applied artificial intelligence (AI) techniques to develop a reliable model for the prediction of GWQI by employing MLP neural network and SVR models. In this stage, the input data were the detected physiochemical parameters, and the output was the computed GWQI. The dataset was divided into two groups with a ratio of 80% to 20% for models training and validation. The predicted (AI) and actual (calculated GWQI) models were compared using four statistical criteria, namely, mean square error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R²). Based on the obtained values of the performance measures, the results revealed the robustness and efficiency of MLP and SVR models in modeling GWQI. Consequently, groundwater quality in the north Khartoum area is evaluated as suitable for human consumption except for BH 18, where highly mineralized water is observed. The developed approach is advantageous in groundwater quality evaluation and is recommended to be incorporated in groundwater quality modeling.

Forty-eight water samples (30 groundwater and 18 surface water samples) were collected from the study region. Physical and chemical examinations were performed on the water samples to determine the values of various variables. Several graphs, sheets, and statistical measures, including the sodium solubility percentage (SSP), the sodium absorption ratio (SAR), and Piper’s diagram, were used to plot the concentration of the principal ions and the chloride mass balance (CMB). The contents of the variables were compared with the contents in other local areas and the standard allowable safe limits as recommended by the World Health Organization (WHO). Water pH values were neutral for all water samples. Electric conductivity (EC) readings revealed that water samples vacillated from slightly mineralized to excessively mineralized. Water salinities were fresh and very fresh according to the total dissolved solids (TDS) amounts. The hardness of water ranged from medium to hard in the surface water and from medium to very hard in the groundwater samples. Bicarbonate, sodium, and calcium made up the highest amounts in the surface water samples. The highest concentrations of bicarbonate, sulfate, chloride, and sodium were found in the groundwater. Diagrams show the major ion relationships as well as the type and origin of the water. According to Piper’s plots, most of the water samples under investigation were Ca-HCO₃ type, Mg water types, followed by SO₄.Ca-Cl water types. This highlighted the elemental preponderance of bicarbonate and alkaline earth (Ca²⁺ + Mg²⁺). This dominance is caused by evaporite and carbonate minerals dissolving in water because of anthropogenic activities and interaction processes. The groundwater recharge was estimated to be 0.89–1.6 mm/yr based on Chloride Mass Balance. The examined water samples can also be used for cattle, poultry, and irrigation. Additionally, the groundwater is of poorer quality than the surface water, although both types of water are adequate for various industries, with a range of 14 to 94 percent. With the exception of a few groundwater samples, the tested water samples are suitable for a number of applications.

The primary goals of this research are to detect the spatial variation of the hydrogeological characteristics and evaluate the groundwater quality in the eastern Nile River, Khartoum state, Sudan, using geophysical and hydrochemical methods. Thirteen Vertical electrical soundings (VES), using Schlumberger configuration, were measured along three profiles to characterize the groundwater aquifer. VES findings denoted that the study area comprises two hydraulically connected aquifers. The upper aquifer of sand has an average thickness of 50 m, and the lower aquifer is composed of sandstone of a thickness of up to 300 m. The results of VES inversion were further used to measure aquifer characteristics, including transverse resistance, longitudinal conductance, hydraulic conductivity, and transmissivity. The detected average values of these parameters were 6690 Ωm², 1.4 Ω⁻¹, 264 m²/d and 4 m/day, respectively. In addition, regression analysis was performed to suggest local relationships for estimating aquifer characteristics within the study area. On the other hand, total longitudinal conductance was used to predict the protective strength of the hydrogeological columns, ranging from 1.7 to 5.8 Ω⁻¹; as a result, the protective capacity of the aquifer ranged from good to very good, suggesting potable water quality. This result was subsequently confirmed by the groundwater quality index (GWQI) model. Eleven physiochemical parameters analyzed for nine boreholes were used in GWQI estimation to assess groundwater quality in the study area. The primary analysis of the hydrochemical parameters indicated that almost all parameters are below permissible limits prescribed by the World Health Organization (WHO). The computed GWQI varies between 34.8 and 148, and the majority of groundwater samples, precisely 55.5%, are good water types, while 22.2% of the samples are in an excellent quality state. This research concluded that the groundwater aquifer in the study area is ideal for groundwater exploitation. However, applying a detailed geophysical and hydrochemical survey is recommended to reduce the uncertainty of the resulting models.