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

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