Comparative evaluation of machine learning for prediction of water quality index in constructed wetlands

Constructed wetlands play a crucial role in urban runoff treatment, enhancing water quality and maintaining ecosystem health, while the water quality index (WQI) serves as a key parameter for evaluating their performance. This study provides a comprehensive assessment of WQI prediction in a constructed wetland at Universiti Sains Malaysia, using 442 samples and 11 physicochemical parameters evaluated across six input scenarios. Feature selection was performed using Pearson correlation and feature-importance rankings from extreme gradient boosting (XGBoost) and categorical boosting (CatBoost) to create reduced-input combinations. SHapley Additive exPlanations (SHAP) analysis further indicated that WQI predictions were mainly driven by organic/solid load and nitrogen-related variables (e.g., chemical oxygen demand (COD), total suspended solids (TSS), and ammoniacal nitrogen (AN)). Fourteen ML models, including adaptive boosting (AdaBoost), adaptive neuro-fuzzy inference system, artificial neural network (ANN), CatBoost, extreme learning machine, gradient boosting regressor, histogram gradient boosting (HGB), Huber regressor, multiple linear regression, ridge regression, stochastic gradient descent regressor (SGD), support vector regression (SVR), XGBoost, and a hybrid Grey Wolf Optimizer-ANN, were developed and evaluated using four statistical metrics such as root mean square error (RMSE), coefficient of determination (R2), percent bias (PBIAS), and mean absolute relative error (MARE), complemented by LP-based multi-metric ranking. Across all scenarios (mean LP; lower is better), CatBoost (0.44) and HGB (0.46) achieved the best overall performance, while SGD (0.91) and SVR (0.75) ranked worst. Notably, several top-performing models maintained competitive performance under reduced inputs (e.g., CatBoost's LP value of 0.56 in the four-feature scenario), supporting practical WQI estimation when monitoring variables are limited or costly. These findings highlight the critical role of both input selection and model choice in developing robust, scalable frameworks for WQI prediction.