Stacking Artificial Intelligence Models for Predicting Water Quality Parameters in Rivers

Scrutinizing the changes in the quality of river water is one of the main factors of monitoring the quality of natural flows, which plays a crucial role in the sustainable management of these ecosystems. The concentration of dis-solved oxygen (DO) in river water is one of the most important indicators of quality management in such water bodies. From an environmental point of view, exceeding the permissible and natural decay capacity of pollutants in natural streams leads to a decrease in DO and, consequently, causes serious risks for the survival of aquatic life in related ecosystems. Hence, in the present study, 10 daily variables with the amount of dissolved oxygen on the same day were collected and evaluated from Allen County. Moreover, half of these variables were chosen as effective inputs to the model based on statistical analysis, so as to calculate the dissolved oxygen concentration parameter. Modeling with artificial intelligence approaches was implemented in the form of four individual methods: ANFIS-PSO, OS-ELM, Bagging-RF and Boosting CART, and two ensemble-stacking methods: SMA and Meta-learner MLP. The outcomes of estimating the DO with RMSE, MAE, GRI, r, and MBE criteria and marginal-scatter and subject profile diagrams were discussed. Moreover, the efficiency of the models in estimat-ing the outlier of the observational data was scrutinized by subject profile diagram. Finally, it was found that the Meta-learner MLP model with RMSE of 0.965 mg/L had improvement in performance by 8.8%, 8.9%, 22.3%, 24.9% and 27.6%, respectively, compared to SMA, Boosting CART, Bagging-RF, ANFIS-PSO and OS-ELM methods. This remarkable improvement led to recommendations for using stacking techniques in water quality modeling and simulation.