Water quality monitoring is crucial in managing water resources and ensuring their safety for human use and environmental health. In the Al-Jawf Basin, we conducted a study on the Quaternary aquifer, where various techniques were utilized to evaluate, simulate, and predict the groundwater quality (GWQ) for irrigation. These techniques include water quality indices (IWQIs), geochemical modeling, multivariate statistical analysis, geographic information systems (GIS), and adaptive neuro-fuzzy inference systems (ANFIS). Physicochemical analysis was conducted on the collected groundwater samples to determine their composition. The results showed that the order of abundance of ions was Ca²⁺ > Mg²⁺ > Na⁺ > K⁺ and SO₄²⁻ > Cl⁻ > HCO₃⁻ > NO₃⁻. The assessment of groundwater quality for irrigation based on indices such as Irrigation water quality index (IWQI), sodium adsorption ratio(SAR), sodium percent (Na%), soluble sodium percentage (SSP), potential salinity (PS), and residual sodium carbonate RSC, which revealed moderate-to-severe restrictions in some samples. The Adaptive Neuro-Fuzzy Inference System (ANFIS) model was then used to predict the IWQIs with high accuracy during both the training and testing phases. Overall, these findings provide valuable information for decision-makers in water quality management and can aid in the sustainable development of water resources.

This study developed an integrated hydrogeochemical-machine learning approach for groundwater quality assessment and health risk evaluation in Yemen’s Al Jawf basin. Thirty-three samples from the Quaternary aquifer were analyzed for major ions and trace metals, with multivariate statistics identifying hydrogeochemical patterns and Random Forest (RF) models predicting water quality indices. Hydrogeochemical analysis revealed three water types: Ca-HCO₃ (45%), mixed Ca-Mg-HCO₃-SO₄ (36%), and Na-Cl (19%) facies, controlled by carbonate dissolution, silicate weathering, and anthropogenic inputs. Principal component analysis identified three components explaining 78.5% of variance, with natural mineralization dominating (45.2%). Irrigation water quality index (IWQI) assessment classified 52% of samples as excellent-good, 33% as moderate, and 15% as poor-very poor. Significant samples (63.5-93%) in eastern and northwestern areas showed potential negative impacts on soil fertility based on irrigation indices. Health risk evaluation revealed that 27% of samples exceeded drinking guidelines for Fe and Mn, with children more vulnerable than adults according to hazard index value (HI > 1). Random Forest models achieved high accuracy (R² = 0.94–0.97) predicting multiple indices using 6–8 parameters, reducing monitoring costs by 60%. This represents the first post-conflict comprehensive assessment in Al Jawf basin, providing a replicable framework for sustainable groundwater management in arid regions with limited resources.