A long-term prediction of domestic water demand using preprocessing in artificial neural network

Research Article| October 17 2013 A long-term prediction of domestic water demand using preprocessing in artificial neural network Sadegh Behboudian; Sadegh Behboudian 1College of Engineering, School of Civil Engineering, University of Tehran, PO Box 11155-4563, Tehran, Iran E-mail: s.behboudian@gmail.com Search for other works by this author on: This Site PubMed Google Scholar Massoud Tabesh; Massoud Tabesh 2Center of Excellence for Engineering and Management Civil Infrastructures, College 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 Maliheh Falahnezhad; Maliheh Falahnezhad 3Department 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 Farrokh Alavian Ghavanini Farrokh Alavian Ghavanini 1College 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 (2014) 63 (1): 31–42. https://doi.org/10.2166/aqua.2013.085 Article history Received: April 23 2013 Accepted: August 30 2013 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Share Icon Share MailTo Twitter LinkedIn 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 Sadegh Behboudian, Massoud Tabesh, Maliheh Falahnezhad, Farrokh Alavian Ghavanini; A long-term prediction of domestic water demand using preprocessing in artificial neural network. Journal of Water Supply: Research and Technology-Aqua 1 February 2014; 63 (1): 31–42. doi: https://doi.org/10.2166/aqua.2013.085 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Both planning and the design of water supply systems require accurate and reliable prediction of water demand. In this study, artificial neural network (ANN) was used to predict the long-term water demand to determine the relationship between dependent and independent parameters. Using the stationary chain to solve the interpolating characteristic of ANNs, the study presents a reliable approach for long-term forecasting of water demand. The purpose of this study is to provide a convenient and reliable method for long-term forecasting of urban water demand while reducing the prediction uncertainty. In order to evaluate the accuracy of the prediction, multilayer perceptron (MLP) outputs were compared with results from the linear regression model. Findings indicate that MLP is an appropriate solution for monthly long-term water demand forecasting. Furthermore, it can reduce uncertainties and significantly increase the accuracy of the long-term forecasting. artificial neural network, long-term forecasting, preprocessing, water demand This content is only available as a PDF.