Machine Learning Classifies Groundwater Quality with 99.4% Accuracy

Researchers developed a machine learning framework that accurately classifies groundwater quality. The XGBoost algorithm achieved the best performance, with 99.40% accuracy and an F1-score of 0.994.

This study developed a rapid machine learning framework for classifying groundwater quality using physicochemical parameters. It utilized 5006 groundwater records from India, covering 2018-2022, to categorize samples into Acceptable, Needs Treatment, and Hazardous classes based on CPCB guidelines.

The abstract focuses on findings; it does not give personal how-to steps.

This study presents a machine learning-driven framework for groundwater quality classification. It analyzed 5006 groundwater records from India (2018-2022) to develop a regulation-driven decision-support tool. The framework aims to support sustainable groundwater governance and water security initiatives.