Groundwater fluoride prediction modeling using physicochemical parameters in Punjab, India: a machine-learning approach - Kerketta, Anjali; Kapoor, Harmanpreet Singh; Sahoo, Prafulla Kumar (Frontiers in Soil Science, 2024)
Rising fluoride levels in groundwater resources have become a worldwide concern, presenting a significant challenge to the safe utilization of water resources and posing potential risks to human well-being. Elevated fluoride and its vast spatial variability have been documented across different districts of Punjab, India, and it is, therefore, imperative to predict the fluoride levels for efficient planning and management. In this study, five different models, Support Vector Machine (SVM), Random Forest (RF), Extreme Gradient Boosting (Xgboost), Extreme Learning Machine (ELM), and Multilayer perceptron (MLP), are proposed to predict groundwater-fluoride using the physicochemical parameters and sampling depth as predictor variables. The performance of these five models was evaluated using the coefficient of determination (R 2 ), mean absolute error (MAE), and root mean square error (RMSE). ELM outperformed the remaining four models, thus exhibiting a strong predictive power. The R 2 , MAE, and RMSE values for ELM at the training and testing stages were 0.85, 0.46, 0.36 and and, 0.95, 0.305, and 0.33, respectively, while other models yielded extremely poinferior results. Based on the relative importance scores, total dissolved solids (TDS), electrical conductivity (EC), sodium (Na + ), and chloride (Clˉ), and calcium (Ca 2+ ) contributed significantly in to model performance. High variability in the target (fluoride) and predictor variables might have led to the poor performance of the models, implying the need for better data preprocessing techniques to improve data quality. Although ELM showed satisfactory results, it can be considered as a promising model for predicting groundwater quality.