Extraction of Major Groundwater Ions from Total Dissolved Solids and Mineralization Using Artificial Neural Networks: A Case Study of the Aflou_Syncline Region, Algeria

Global water demand due to population growth and agricultural development, has led to widespread overexploitation of groundwater, particularly in semi-arid regions. Traditional hydrochemistry monitoring system still suffers from limited laboratory accessibility and high costs. This study aims to predict major ions of groundwater, including Ca²⁺, Mg²⁺, Na⁺, SO₄²⁻, Cl⁻, K⁺, HCO₃⁻, and NO₃⁻, utilizing two field measurable parameters (i.e., total dissolved solids (TDS) and mineralization (MIN)) in Aflou_Syncline region, Algeria. A multilayer perceptron (MLP) model optimized with the Levenberg-Marquardt backpropagation (LMBP) provided the most predictive accuracy for the different ions of SO₄²⁻, Mg²⁺, Na⁺, Ca²⁺, and Cl⁻ with R2 = (0.842, 0.980, 0.759, 0.945, 0.895) and RMSE = (53.660, 12.840, 14.960, 36.460, 30.530) (mg/L) in the testing phase, respectively. However, the predictive accuracy for the remaining ions of K⁺, HCO₃⁻, and NO₃⁻ was supplied as R² = (0.045, 0.366, 0.004) and RMSE = (6.480, 41.720, 40.460) (mg/L), respectively. The performance of our model (LMBP-MLP) was validated in similar geological areas in the adjacent area, including Aflou, Madna, and Ain Madhi. In addition, LMBP-MLP showed very promising results, with performance similar to the original research area.