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This research aims to assess the ability of machine learning models to predict water quality index from groundwater physicochemical parameters.

April 8, 2026

Machine Learning Models for the Prediction of Water Quality Index Using Physicochemical Characteristics of Groundwater

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Research demonstrates machine learning methods predict water quality index in groundwater, suggesting efficient resource management.

This research aims to assess the ability of machine learning models to predict water quality index from groundwater physicochemical parameters.
Analyzed a dataset of 409 groundwater samples with various physicochemical characteristics.
Calculated the water quality index (WQI) using various regression models.
Evaluated Decision Tree, Random Forest, Gradient Boosting, and XGBoost Regressors based on statistical metrics.
The Gradient Boosting Regressor showed the highest predictive capability with R2 = 0.8982.
XGBoost and Random Forest models also exhibited strong predictive abilities.
Decision Tree model performed with relatively low accuracy compared to others.

Panneerselvam et al. (2026) studied this question.

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