Machine Learning (ML) algorithms provide an alternative for the prediction of pollutant concentration. We compared eight ML (Linear Regression (LR), uniform weighting k-Nearest Neighbor (UW-kNN), variable (VW-kNN), Support Vector (SVR), Artificial Neural Network (ANN), Tree (RT), Random Forest (RF), and Adaptive Boosting (AdB)) to evaluate feasibility approaches estimation Total Suspended Solid...