Comprehensive analysis of multiple classifiers for enhanced river water quality monitoring with explainable AI

Monitoring river water quality is crucial for safeguarding public health, protecting ecosystems, and ensuring economic sustainability. It helps detect contaminants, ensures drinking water safety, and facilitates early intervention for environmental protection and legal compliance. The objective of this study is to evaluate multiple machine learning algorithms to analyze water quality parameters in computing water quality index (WQI) and classification thereof, aiming to devise a reliable method for forecasting water quality with high accuracy. In this study, fourteen machine learning classifiers applied include Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), Decision Tree (DT), Multilayer Perceptron (MLP), K-Nearest Neighbor (KNN), Naïve Bayes, Gradient boosting, AdaBoost, Bagging, Extra Trees, Quadratic Discriminant Analysis (QDA), XGBoost, and CATBoost. A total of 1096 sample data was used where each data consists of nineteen analytical water quality parameters. To assess the performance of various classifiers, several evaluation techniques were utilized including confusion matrices, classification reports detailing precision and accuracy ratios, and Receiver Operating Characteristic (ROC) curves. The study also utilizes explainable AI (LIME and SHAP) to provide clear insights into the decision-making processes used to classify river water quality. The results indicated that all ML models demonstrate satisfactory performance in predicting WQI. Among the classifiers used, Gradient Boosting achieves the highest Accuracy (99.64 %), Precision (0.95), Recall (0.96), and F1-Score (0.95), indicating its superior ability to correctly classify instances and suggesting a balanced performance across different evaluation metrics. The analysis presented in this article holds the promise of providing accurate water quality data to researchers, thereby enhancing monitoring effectiveness through the application of machine learning techniques.