Artificial neural network modeling of the effluent quality index for municipal waste water treatment plants using quality variables: south of Tehran waste water treatment plant

Abstract

Research Article| September 29 2015 Artificial neural network modeling of the effluent quality index for municipal wastewater treatment plants using quality variables: south of Tehran wastewater treatment plant Maliheh Falah Nezhad; Maliheh Falah Nezhad 1Department of Environmental Engineering, Graduate Faculty of Environment, University of Tehran, Ghods Street, Tehran, Iran E-mail: m_falahnezhad@yahoo.com Search for other works by this author on: This Site PubMed Google Scholar Naser Mehrdadi; Naser Mehrdadi 1Department of Environmental Engineering, Graduate Faculty of Environment, University of Tehran, Ghods Street, Tehran, Iran Search for other works by this author on: This Site PubMed Google Scholar Ali Torabian; Ali Torabian 1Department of Environmental Engineering, Graduate Faculty of Environment, University of Tehran, Ghods Street, Tehran, Iran Search for other works by this author on: This Site PubMed Google Scholar Sadegh Behboudian Sadegh Behboudian 2College of Engineering, School of Civil Engineering, University of Tehran, PO Box 11155-4563, Tehran, Iran Search for other works by this author on: This Site PubMed Google Scholar Journal of Water Supply: Research and Technology-Aqua (2016) 65 (1): 18–27. https://doi.org/10.2166/aqua.2015.030 Article history Received: February 28 2015 Accepted: August 20 2015 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Share Icon Share Facebook Twitter LinkedIn Email Tools Icon Tools Cite Icon Cite Permissions Search Site Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll JournalsThis Journal Search Advanced Search Citation Maliheh Falah Nezhad, Naser Mehrdadi, Ali Torabian, Sadegh Behboudian; Artificial neural network modeling of the effluent quality index for municipal wastewater treatment plants using quality variables: south of Tehran wastewater treatment plant. Journal of Water Supply: Research and Technology-Aqua 16 February 2016; 65 (1): 18–27. doi: https://doi.org/10.2166/aqua.2015.030 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Using treated wastewater could be a realistic solution for the scarcity of fresh water in Iran; therefore, effluent quality evaluation is an essential task. In the present study, the effluent quality index (EQI) for south of Tehran municipal wastewater treatment plant was found considering eight quality parameters and sub-index formulae method. Further, with the help of an artificial neural network (ANN), design and applicability of feed-forward, three-layer perceptron neural network model for computing the EQI has been assessed. In this study, ANN modeling is done by ANN toolbox in MATLAB 2013. The modeling efforts showed that the optimal network architecture was 8–7–1 and that the best EQI predictions were associated with the Levenberg–Marquardt back propagation training algorithm and Tansig transfer function. The EQI predictions of this model had significant and very high correlation (R = 0.96, MSE = 0.1) with the measured EQI values. The ANN approach which is used in this article suggested powerful tool to EQI computation and prediction, since it reduced lengthy computations and using various sub-index formula for each value. artificial neural networks, back-propagation, effluent quality index, municipal wastewater treatment plant, sub-index © IWA Publishing 2016 You do not currently have access to this content.