Assessment of Groundwater Quality of Madhya Pradesh, India using Data-Driven Approaches

Groundwater quality assessment is crucial for ensuring safe drinking water and sustainable resource management. However, traditional monitoring methods involving extensive sampling and laboratory analysis are time-consuming and costly. The present study proposes an efficient approach for predicting groundwater quality in Madhya Pradesh, India using data-driven models and an entropy-weighted water quality index (EWQI). A large spatiotemporal dataset of different parameters of groundwater quality like pH, total dissolved solids (TDS), calcium (Ca²⁺), total hardness (TH), nitrate (NO₃⁻), sodium (Na⁺), chloride (Cl⁻), potassium (K⁺), sulfate (SO₄²⁻), magnesium (Mg²⁺), and fluoride (F⁻) from the year (2003-2023) across Madhya Pradesh was analysed. All advanced data-driven models such as Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Artificial Neural Network (ANN), and Support Vector Machine (SVM) were developed to predict the EWQI using easily measurable parameters pH, TH and TDS. The individual ability of the models was assessed using statistical analysis with the coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE). During the training phase, all models such as RF, SVM, XGBoost, and ANN proved excellent predictive capabilities, achieving an R² value exceeding 0.90 while maintaining minimal errors when pH, TH, and TDS were considered as input variables. The overall outcomes confirmed that the data-driven models could accurately estimate the EWQI, closely matching the actual values with an R² greater than 0.90. This finding highlights the model's ability to predict a reliable overview of water quality for a small area using easily measurable parameters.

Keywords: Groundwater, Data-driven, Drinking water, Water Quality Index, Machine learning