Amirkabir University of Technology Amirkabir Journal of Civil Engineering 2588-297X 53 11 2022 01 21 Using Ensemble Model to Improve ANN, ANFIS, SVR Models in Predicting Effluent BOD and COD Using Ensemble Model to Improve ANN, ANFIS, SVR Models in Predicting Effluent BOD and COD 4683 4702 4176 10.22060/ceej.2020.18441.6873 FA Parisa Asghari Department of Water Resources Engineering, Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran Vahid Nourani Department of Water Resources Engineering, Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran Elnaz Sharghi Department of Water Resources Engineering, Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran Nazanin Behfar Department of Water Resources Engineering, Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran 0000-0002-8211-4006 Journal Article 2020 05 15 In this study, black box artificial intelligence models (AI) including feed-forward neural network (FFNN), support vector regression (SVR), and adaptive neuro-fuzzy inference system (ANFIS) were used to predict effluent biological oxygen demand (BODeff) and chemical oxygen demand (CODeff) of Tabriz wastewater treatment plant (WWTP) using the daily data collected from 2016 to 2018. In addition, the autoregressive integrated moving average (ARIMA) linear model was used to predict BODeff and CODeff parameters to compare the linear and non-linear models' abilities in complex processes prediction. To improve the prediction of BODeff and CODeff parameters, the data post-processing ensemble method was also used. The input data set included daily influent BOD, COD, total suspended solids (TSS), pH at the current time (t), and BODeff and CODeff at the previous time (t-1) and the output data included BODeff and CODeff at t. The results of the single models indicated that the SVR model provides better results than the other single models. In ensemble modeling, simple and weighted linear averaging, and neural network ensemble methods were applied to enhance the performance of the single AI models. The results indicated that using ensemble models could increase the prediction accuracy up to 15% at the verification phase. In this study, black box artificial intelligence models (AI) including feed-forward neural network (FFNN), support vector regression (SVR), and adaptive neuro-fuzzy inference system (ANFIS) were used to predict effluent biological oxygen demand (BODeff) and chemical oxygen demand (CODeff) of Tabriz wastewater treatment plant (WWTP) using the daily data collected from 2016 to 2018. In addition, the autoregressive integrated moving average (ARIMA) linear model was used to predict BODeff and CODeff parameters to compare the linear and non-linear models' abilities in complex processes prediction. To improve the prediction of BODeff and CODeff parameters, the data post-processing ensemble method was also used. The input data set included daily influent BOD, COD, total suspended solids (TSS), pH at the current time (t), and BODeff and CODeff at the previous time (t-1) and the output data included BODeff and CODeff at t. The results of the single models indicated that the SVR model provides better results than the other single models. In ensemble modeling, simple and weighted linear averaging, and neural network ensemble methods were applied to enhance the performance of the single AI models. The results indicated that using ensemble models could increase the prediction accuracy up to 15% at the verification phase.

https://ceej.aut.ac.ir/article_4176_16d11e9595188dbad0418a85f0351aba.pdf