The recent research aims to investigate the petrophysical and hydrogeological parameters of the Nubian aquifer system (NAS) in Northern Khartoum State, Sudan, using integrated geophysical methods, including surface electrical resistivity and geophysical well-logging. The Nubian aquifer is a transboundary regional aquifer that covers vast areas in Sudan, Egypt, Libya and Chad. The well-logs, including self-potential (SP), natural gamma ray (GR), and long normal resistivity (RS), are integrated with Vertical Electrical Sounding (VES) measurements to delineate the hydrostratigraphical units. As a result, two aquifers are detected. An upper aquifer comprises coarse sand with an average thickness of 50 m and a lower aquifer of sandstone with more than 200 m thickness. For a thorough evaluation of the aquifers, in the first stage, the petrophysical and hydrogeological parameters, including formation factor, total and effective porosity, shale volume, hydraulic conductivity, and transmissivity, are measured solely from geophysical well-logs. In the second step, the results of geophysical well logs are combined with VES and pumping test data to detect the spatial variation of the measured parameters over the study area. As a result, the hydraulic conductivity of the Nubian aquifers ranged from 1.9 to 7.8 m/day, while the transmissivity varied between 120 and 733 m2/day. These results indicated that the potentiality of the Nubian formation is high; however, in some regions, due to the sediment heterogeneity, the aquifers have intermediate to high potential. According to the obtained results, it can be concluded that the Nubian Aquifer in Khartoum state is ideal for groundwater development. This research discovered that geophysical approaches can be used to characterize moderately heterogeneous groundwater systems by comparing the Nubian aquifer with similar aquifer systems that have similar hydrogeological settings. This study emphasized the application of universal principles in extrapolating hydraulic parameters in hydrogeophysical surveys. This approach aims to reduce the costs and efforts associated with traditional hydrogeological approaches. ... Read more

Agriculture is considered one of the primary elements for socioeconomic stability in most parts of Sudan. Consequently, the irrigation water should be properly managed to achieve sustainable crop yield and soil fertility. This research aims to predict the irrigation indices of sodium adsorption ratio (SAR), sodium percentage (Na%), permeability index (PI), and potential salinity (PS) using innovative machine learning (ML) techniques, including K-nearest neighbor (KNN), random forest (RF), support vector regression (SVR), and Gaussian process regression (GPR). Thirty-seven groundwater samples are collected and analyzed for twelve physiochemical parameters (TDS, pH, EC, TH, Ca+2, Mg+2, Na+, HCO3−, Cl, SO4−2, and NO3−) to assess the hydrochemical characteristics of groundwater and its suitability for irrigation purposes. The primary investigation indicated that the samples are dominated by Ca-Mg-HCO3 and Na-HCO3 water types resulted from groundwater recharge and ion exchange reactions. The observed irrigation indices of SAR, Na%, PI, and PS showed average values of 7, 42.5%, 64.7%, and 0.5, respectively. The ML modeling is based on the ion’s concentration as input and the observed values of the indices as output. The data is divided into two sets for training (70%) and validation (30%), and the models are validated using a 10-fold cross-validation technique. The models are tested with three statistical criteria, including mean square error (MSE), root means square error (RMSE), and correlation coefficient (R2). The SVR algorithm showed the best performance in predicting the irrigation indices, with the lowest RMSE value of 1.45 for SAR. The RMSE values for the other indices, Na%, PI, and PS, were 6.70, 7.10, and 0.55, respectively. The models were applied to digital predictive data in the Nile River area of Khartoum state, and the uncertainty of the maps was estimated by running the models 10 times iteratively. The standard deviation maps were generated to assess the model’s sensitivity to the data, and the uncertainty of the model can be used to identify areas where a denser sampling is needed to improve the accuracy of the irrigation indices estimates. ... Read more