Groundwater Quality Assessment for Drinking and Irrigation Purposes at Al-Jouf Area in KSA Using Artificial Neural Network, GIS, and Multivariate Statistical Techniques

Abstract

Groundwater is an essential resource for drinking and agricultural purposes in the Al-Jouf region, Saudi Arabia. The main objective of this study is to assess groundwater quality for drinking and irrigation purposes in the Al-Jouf region. Physicochemical characteristics of groundwater were determined, including total dissolved solids (TDS), pH, electric conductivity (EC), hardness, and various anions and cations. The groundwater quality index (WQI) was calculated to determine the suitability of groundwater for drinking purposes. The EC, sodium percentage (Na+ %), magnesium hazard (MH), sodium adsorption ratio (SAR), potential salinity (PS), and Kelley’s ratio (KR) were assessed to evaluate the suitability of groundwater for irrigation. Effective statistical tests and Feed-forward neural network (FFNN) modeling were applied to reveal the correlation between parameters and predict WQI. The results indicated that approximately all samples are appropriate for drinking and irrigation uses except samples of the Al Qaryat region. The ionic abundance ranking was Na+ > Ca2+ > Mg2+ > K+ for cations, and Cl− > SO42− > NO3− for anions. Moreover, the groundwater is dominated by alkali metals (K+ and Na+) and controlled by the rock–water interaction process. The indicators of groundwater quality for irrigation and drinking according to the following criteria (Na+ %, SAR, KR, MH, PS, WQI (WHO), and WQI (BIS)) can be predicted by the FFNN with root mean square errors (RMSE) of 0.136, 0.070, 0.022, 0.073, 2.45 × 10−3, 1.45 × 10−2, and 1.18 × 10−2, respectively, and R2 of 0.99, 1.00, 0.99, 0.99, 1.00, 1.00, and 1.00, respectively.