What question did this study set out to answer?

This research aims to predict the Irrigation Water Quality Index (IWQI) using machine learning while ensuring interpretability through SHAP analysis.

April 19, 2026Open Access

Machine Learning-Based Prediction of Irrigation Water Quality Index with SHAP Interpretability: Application to Groundwater Resources in the Semi-Arid Region, Algeria

Key Points

This research aims to predict the Irrigation Water Quality Index (IWQI) using machine learning while ensuring interpretability through SHAP analysis.
Analyzed 191 groundwater samples from the Ain Oussera plain for major ions and physicochemical parameters.
Calculated multiple irrigation suitability indices, including SAR and others.
Developed and evaluated five machine learning models: Random Forest, Gradient Boosting, XGBoost, K-Nearest Neighbors, and Support Vector Regression.
Applied SHAP analysis for model interpretability, identifying key influencing parameters.
Mapped spatial variability of water quality using Inverse Distance Weighting in ArcGIS.
55% of groundwater samples showed low to no restrictions for irrigation.
19% of samples required high to severe restrictions.
The XGBoost model achieved the highest R2 of 0.95 and the lowest RMSE of 3.22.
Electrical conductivity and Sodium Adsorption Ratio were the most influential parameters on IWQI.
Significant spatial variability in water quality was revealed throughout the plain.

Abstract

In semi-arid regions, sustainable groundwater management for irrigation is critical for agricultural productivity and food security. This study presents an integrated methodological framework combining hydrochemical characterization, machine learning (ML) modeling, and explainable artificial intelligence (XAI) to predict the Irrigation Water Quality Index (IWQI) in the Ain Oussera plain, Djelfa Province, Algeria. A total of 191 groundwater samples were collected from November 2023 to September 2024 and analyzed for major ions and physicochemical parameters. Multiple irrigation suitability indices were calculated, including Sodium Adsorption Ratio (SAR), Sodium Percentage (Na%), Magnesium Hazard (MH), Permeability Index (PI), Residual Sodium Carbonate (RSC), Soluble Sodium Percentage (SSP), and Kelly’s Ratio (KR). Five ML models were developed and evaluated for IWQI prediction: Random Forest, Gradient Boosting, XGBoost, K-Nearest Neighbors, and Support Vector Regression. Results showed that 55% of groundwater samples exhibited low to no restrictions for irrigation use, while 19% required high to severe restrictions. The XGBoost model demonstrated superior performance, with the highest R2 (0.95) and the lowest RMSE (3.22) among all tested algorithms. SHAP (SHapley Additive exPlanations) analysis provided a transparent interpretation of model predictions, identifying electrical conductivity and Sodium Adsorption Ratio as the most influential parameters affecting IWQI, while chloride, sodium, total hardness, and magnesium had minimal impact. Spatial mapping using Inverse Distance Weighting (IDW) interpolation in ArcGIS 10.8 revealed considerable spatial variability in water quality throughout s the plain. This research addresses a critical gap in North African groundwater management by integrating ML predictive capabilities with XAI transparency, providing water resource managers and agricultural stakeholders with interpretable, data-driven tools for sustainable irrigation planning in water-stressed semi-arid environments.

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