Groundwater is eventually undermined by human activities, such as rapid industrialization, urbanization, over-extraction, and contamination from agricultural and urban sources. Among the different contaminants, the presence of minerals such as calcium (Ca), magnesium (Mg), sodium (Na), potassium (K), fluoride (F), and chloride (Cl) proves to have serious health risks when present in excess concentrations. This study addresses this gap by developing a predictive machine learning model to evaluate the groundwater quality index (GWQI) and to identify the critical contaminants affecting water safety. A total of 1989 groundwater samples were collected from Jajpur district, where the Sukinda Valley is located, and analyzed for multiple physicochemical parameters, as this region is known for extensive chromite mining activities and has been identified as one of the most critically polluted areas in India, posing significant groundwater contamination risks. This study introduces the novel hybrid machine learning model, LCBoost fusion, which distinguishes this work from previous studies by combining the strengths of CatBoost and LightGBM to enhance predictive accuracy. It has been achieved with the help of a hybrid machine learning model i.e. LCBoost fusion. The model outperforms individual models (CatBoost and LightGBM), by achieving low RMSE (0.6826), MSE (0.5100), MAE (0.3148) and a high \(\textrm{R}^2\) score of 0.9810. Feature importance analysis highlights potassium (K), fluoride (F) and total hardness (TH) as the most influential indicators of groundwater contamination. This research successfully demonstrates the application of machine learning in assessing groundwater quality risks in Odisha, with practical implications for real-time groundwater monitoring and risk mitigation. LCBoost Fusion model offers a reliable and efficient approach for real-time groundwater monitoring and risk mitigation. These findings will help environmental organizations and policy makers to map out targeted places for sustainable groundwater management.

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.

Access to clean drinking water is crucial for any region’s social and economic growth. However, rapid urbanization and industrialization have significantly deteriorated water quality, posing severe pollution threats from domestic, agricultural, and industrial sources. This study presents an innovative framework for assessing water quality in North Bhubaneswar, integrating the Water Quality Index (WQI) with statistical analysis, geospatial technologies, and machine learning models. The WQI, calculated using the Weighted Arithmetic Index method, provides a single composite value representing overall water quality based on several key physicochemical parameters. To evaluate potable water quality across 21 wards in the northern zone, several key parameters were monitored, including pH, electrical conductivity (EC), dissolved oxygen (DO), hardness, chloride, total dissolved solids (TDSs), and biochemical oxygen demand (BOD). The Weighted Arithmetic WQI method was employed to determine overall water quality, which ranged from excellent to good. Furthermore, Principal Component Analysis (PCA) revealed a strong positive correlation (r > 0.6) between pH, conductivity, hardness, and alkalinity. To enhance the accuracy and reliability of water quality assessment, multiple machine learning models Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naïve Bayes (NB) were applied to classify water quality based on these parameters. Among them, the Decision Tree (DT) and Random Forest (RF) models demonstrated the highest precision (91.8% and 92.7%, respectively) and overall accuracy (91.7%), making them the most effective in predicting water quality and integrating WQI, machine learning, and statistics to analyze water quality. The study emphasizes the importance of continuous water quality monitoring and offers data-driven recommendations to ensure sustainable access to clean drinking water in North Bhubaneswar.