From data to decision: leveraging machine learning and water quality index for groundwater quality evaluation

Groundwater quality is critical for sustainable development, serving as a primary source of drinking water and irrigation. The present study employs the machine learning (ML) models to evaluate the water quality index (WQI) in order to enhance the groundwater quality assessment. Forty groundwater samples were collected from six diverse locations and analyzed for seven physicochemical parameters, including pH, Turbidity, CO₂, Chloride, Alkalinity, TDS, and Fe. To improve model generalizability, data augmentation techniques, Gaussian noise and interpolation, expanded the dataset to 120 samples. WQI was computed using the Canadian Council of Ministers of the Environment (CCME) method. Six ML models were employed for predictive analysis and evaluated based on R2, RMSE, and MAE. The results revealed significant contamination, with 25% of…