Research Article| May 01 2009 Prognosis of urban water consumption using hybrid fuzzy algorithms Gergely Bárdossy; Gergely Bárdossy 1Department of Hydrodynamic Systems, Budapest University of Technology and Economics, Mûegyetem rkp. 3, Budapest, 1111, Hungary Tel.: +36 1 463 3097 Fax.: +36 1 463 3091; E-mail: bardossy@hds.bme.hu Search for other works by this author on: This Site PubMed Google Scholar Gábor Halász; Gábor Halász 1Department of Hydrodynamic Systems, Budapest University of Technology and Economics, Mûegyetem rkp. 3, Budapest, 1111, Hungary Search for other works by this author on: This Site PubMed Google Scholar János Winter János Winter 2Department of Hydraulic and Water Resources Engineering, Budapest University of Technology and Economics, Mûegyetem rkp. 3, Budapest, 1111, Hungary Search for other works by this author on: This Site PubMed Google Scholar Journal of Water Supply: Research and Technology-Aqua (2009) 58 (3): 203–211. https://doi.org/10.2166/aqua.2009.092 Article history Received: August 28 2007 Accepted: November 07 2008 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Share Icon Share Facebook Twitter LinkedIn MailTo 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 Gergely Bárdossy, Gábor Halász, János Winter; Prognosis of urban water consumption using hybrid fuzzy algorithms. Journal of Water Supply: Research and Technology-Aqua 1 May 2009; 58 (3): 203–211. doi: https://doi.org/10.2166/aqua.2009.092 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex For the optimal operation of waterworks it is necessary to predict the expected water consumption of the following days as accurately as possible. However, there are no conventional methods to predict the water demand. In this paper a prediction model based on hybrid fuzzy algorithms is introduced. The software automatically creates a fuzzy rule system out of a training database using the so-called VISIT (Variable Input Spread Inference Training) algorithm. A fuzzy neural network (FNN) system is created. Rules are trained with back propagation (BP) and least squares estimate (LSE) methods. The parameters of the algorithm are optimized with a simple genetic algorithm. As a result, one gets a rule system that delivers higher accuracy than a common statistically based model. Calculations and results are presented in this paper. fuzzy logic, hybrid algorithm, learning, prediction, water consumption This content is only available as a PDF. © IWA Publishing 2009 You do not currently have access to this content.