مشخصات پژوهش

Uncertainty qualification of estimated biological variables of Tabriz wastewater treatment plant (WWTP) in Iran, was conducted by artificial intelligence-based modeling. The pollution variables at entrance of sedimentation section and its output were considered as inputs and outputs of the modeling. Non-linear mutual information (MI) and linear correlation coefficient (CC) were used to select inputs. Modeling was conducted via two feedforward neural networks (FFNN) models for point prediction (PP) and lower and upper bound estimation (LUBE) to provide prediction intervals (PIs) of the computed variables as representative of the modeling uncertainty. In the proposed LUBE, an FFNN was trained with two outputs as upper and lower limits of the predictions. The results showed that selecting the inputs through MI led to narrower PI bound and less uncertainty. Computed determination coefficient (DC)train and DCTest for modeling effluent biological oxygen demand (BODeff) were respectively as 0.9 and 0.7, whereas the values for modeling effluent chemical oxygen demand (CODeff) were 0.8 and 0.7. Comparison of non-linear machine learning (ML) models of FFNN, support vector machine (SVR), adaptive neuro-fuzzy inference system (ANFIS), and linear autoregressive integrated moving average (ARIMA) model demonstrated the superiority of FFNN method for PP task over others. In the LUBE method, the best relationship between pollution parameters was obtained via the MI method. PICP and CWC values were obtained for BOD as 94.08 % and 0.152 and for COD as 86.41 % and 0.1769, respectively. The innovation of this research is to examine the uncertainties in neural network-based analysis of WWTP modeling.