Integrated groundwater quality assessment using geochemical modelling and machine learning approach in Northern India

Groundwater is an essential resource for global drinking and agricultural practices, but it is increasingly threatened by contamination. A comprehensive study was conducted for groundwater quality at 23 different locations in Kasganj, Uttar Pradesh, India, utilizing state-of-the-art Water Quality Indexing (WQI) and Irrigation Water Quality Indexing (IWQI) techniques. A total of one hundred fifteen groundwater samples were analyzed for twelve water quality aspects: pH, total dissolved solids (TDS), total alkalinity, total hardness, calcium (Ca²⁺), magnesium (Mg²⁺), sodium (Na⁺), potassium (K⁺), chloride (Cl^-), bicarbonate (HCO₃^-), Sulphate (SO₄^2-), Nitrate (NO₃^-), and fluoride (F^-). The results revealed that the TDS levels were alarmingly high, spanning 252 to 2054 ppm with an average of 942 ppm. Similarly, fluoride levels, ranging from 0.21 to 3.80 ppm (average 1.55 ppm), exceeded the World Health Organization's permissible limit of 1.5 ppm. Strong correlations among fluoride levels, alkalinity, pH, Na⁺, and HCO₃⁻ point to geochemical interactions causing pollution. Piper diagram analysis divided most samples into Ca-Mg-Cl hydrochemical facies, a classification indicating the dominant ions in the water. Mineral saturation indices indicated dolomite, calcite, and aragonite oversaturation, which means these minerals are present in excess, potentially due to the water's high TDS levels. With WQI scores ranging from 63.64 to 221.18, WQI results were concerning: 60.87% of samples were judged unfit for drinking, and 26.08% were relatively poor. These findings raise serious health concerns for the affected populations. Variations in IWQI indicators-Na%, SAR, MH, and KL ratio-informed irrigation fit for different sites. The use of advanced machine learning models (ANN, RF, XGB) for hydrochemical facies analysis, geochemical modeling, and predictive WQI in the sampled area makes the current study unique. To enhance forecast accuracy and support water management, Machine Learning models (Random Forest (RF), Artificial Neural Network (ANN), and Extreme Gradient Boosting (XGB), were used. The outcomes are indicated by better performance by RF with minimum error values (RMSE: 5.97, MSE: 35.69, MAE: 5.49) and a high R2 value of 0.951. ANN followed closely with an R² of 0.957, while XGB achieved an R² of 0.831. The performance by RF was the best in WQI prediction among the models tested. The results reveal critical groundwater pollution in the Kasganj area, emphasizing the immediate requirement of focused remedial action and effective water management plans.