A Scalable Machine Learning Framework for Hydrological Water Quality Monitoring Using Physicochemical and Microbial Parameters

Monitoring river water quality is essential for environmental sustainability and public health. This study proposes a machine learning (ML)-based framework to model, predict, and classify the Water Quality Index (WQI) using river water samples collected across India. The dataset includes eight physicochemical and microbial parameters: Temperature, pH, Dissolved Oxygen, Biological Oxygen Demand (BOD), Conductivity, Nitrate/Nitrite, Fecal Coliform, and Total Coliform. The WQI was calculated using weighted aggregation and categorized into Excellent, Good, Medium, and Poor classes. Regression and classification models—such as Linear Regression, Random Forest, Gradient Boosting, and Logistic Regression—were evaluated using MAE, RMSE, R2, Accuracy, Precision, Recall, and F1-score. Spatial mapping and exploratory data analysis were conducted to identify regional patterns. Feature importance (Gini and permutation-based) and error analysis enhanced interpretability. The framework achieved over 95% agreement with manual WQI classification, highlighting its effectiveness for real-time, scalable water quality monitoring and policy support.