Innovative machine learning and GIS-integrated framework for predicting irrigation water quality through the insights from Semi-arid Coastal Aquifers in Northeastern Algeria

Given its availability amid the increasing scarcity of surface freshwater, groundwater has become a vital and increasingly relied-upon resource, especially in semi-arid and arid regions. Thus, to ensure groundwater complies with standards before use, continuous monitoring and comprehensive quality assessment are essential. This study aimed to assess the quality of groundwater (GW) in the Skikda aquifer, northeastern Algeria, for irrigation using irrigation water quality indices (IWQIs), multivariate statistical analysis,and machine learning algorithms (MLAs): Random Forest regression (RF), Extreme Gradient Boosting regression (XGBR), and Adaptive Boosting Regression (ABR), integrated with SHAP analysis. Forty-four groundwater samples were collected from the study area during summer and winter seasons andanalysed for temperature, pH, electrical conductivity (EC), turbidity, total dissolved solids (TDS), and concentrations of calcium (Ca²⁺), magnesium (Mg²⁺),sodium (Na⁺),potassium (K⁺), chloride (Cl⁻),bicarbonate (HCO₃⁻), sulfate (SO₄²⁻), and nitrate (NO₃⁻).The dominating hydrochemical facies in the study area were Mg-Ca-SO ₄ , accompanied by the Sodium-Chloride (Na-Cl).Principle Component Analysis (PCA) for summer and winter datasets identified four key components suggesting a strong correlation between variables and factors, with PCA indicating that geochemical processes, such as rock0water interaction and dissolution of evaporite minerals, control the groundwater’s chemical composition.Groundwater quality for irrigation varied across the samples, with most exhibiting moderate to high constraints based on IWQI. Sodium Adsorption Ratio (SAR) and Permeability Index (PI) suggested excellent to good water quality,while Sodium Percent (Na%) and Soluble Sodium Percentage (SSP) indicate a small but significant fraction of inappropriate samples.Magnesium Hazard (MH) and SSP indicated that most samples were safe.Compared to winter, summer samples showed slightly poorer quality (higher Na%, SSP, and lower IWQI), likely due toevaporative solute concentration. Random Forest (RF) modelshowed superior predictive accuracy for all Water Quality Indices (WQIs), with strong validation results for both seasons. These results highlight RF's effectiveness in predicting WQIs and highlight the influence of seasonal geochemical processes on groundwater quality, requiring the development of management strategies for sustainable irrigation.