Fluoride Risk Prognostication: A Pioneering Ensemble Machine Learning Approach for Groundwater Contamination Prediction in Parts of the East Coast of India

Abstract

Groundwater fluoride levels have begun to be a global concern, posing significant challenges to the safe utilization of water resources and mitigating potential impacts on human health. Chronic exposure to elevated levels of naturally occurring fluoride in groundwater affects millions worldwide. Prolonged exposure can lead to health issues such as dental fluorosis and skeletal fluorosis. The World Health Organization (WHO) has established a maximum fluoride concentration guideline of 1.5 mg/L for drinking water. However, groundwater quality is not regularly tested in many regions, leaving communities unaware if water sources, such as wells and springs, contain harmful fluoride levels. In the Balasore area, Odisha, India, rising fluoride concentrations and spatial variability necessitate accurate predictions for effective groundwater management. This article proposes four predictive models, Random Forest (RF), Support Vector Regression (SVR), Gradient Boosting (XGBoost), and Stacking Regressor (SR), to estimate fluoride concentrations using physicochemical parameters and sampling depth as predictor variables. The performance of these models is assessed using the coefficient of determination (accuracy), mean square error (MSE), and mean absolute error (MAE). This article compared fluoride concentrations of machine learning models, including SR, RF, XGBoost, and SVR, in groundwater in Balasore, Odisha. Based on predictive performance, an SR model yielded the lowest MSE and MAE scores at 0.01817 and 0.10327, respectively. These findings underscore the superiority of ensemble learning approaches in addressing complex datasets and provide a robust framework for effective groundwater fluoride management. This article highlights the potential of advanced machine learning in improving public health outcomes in fluoride-affected regions. Finally, the recommendations for decreasing the fluoride concentrations and the guidelines for future research will be proposed.