Abstract
Water is an essential element for the survival of all forms of life. The lack of access to clean and safe water can cause various waterborne diseases. Water quality monitoring is vital for ensuring access to clean and safe water. The Water Quality Index (WQI) is a widely used tool to assess water quality, but traditional Water Quality Index (WQI) methods, despite their utility, often suffer from inconsistencies and limitations. Moreover, these methods are not immune to laboratory and human errors. This study aimed to addresses these challenges by integrating advanced machine learning (ML) techniques to refine WQI predictions. Using a dataset comprising physicochemical parameters, such as pH, Cl-, SO42-, Na+, K+, Ca + 2, Mg + 2, Total Hardness & Total Dissolved Solids from diverse water sources, authors implemented several ML algorithms—including Gradient Boosting, Random Forest, and XGBoost—enhanced with explainable AI (XAI). To develop the prediction models, the dataset was divided into three groups: training (70%), testing (15%) and validating (15%). In order to evaluate the models’ performance, the RMSE, MSE, MAE, and R2 metrics were used in this study. The results of model performance indicated that the Gradient Boosting model has superior predictive capabilities after fine-tuning with 96% accuracy on the test dataset. This study suggests a shift towards leveraging ML for more reliable water quality evaluations, promoting enhanced decision-making in environmental health policies.