Extreme learning machine model for water network management

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dc.authorid0000-0003-0710-0867en_US
dc.contributor.authorSattar, Ahmed M.A.
dc.contributor.authorErtuğrul, Ömer Faruk
dc.contributor.authorGharabaghi, Bahram
dc.contributor.authorMcBean, Edward Arthur
dc.contributor.authorCao, Jiuwen
dc.date.accessioned2019-07-04T13:01:30Z
dc.date.available2019-07-04T13:01:30Z
dc.date.issued2017-04-22en_US
dc.departmentBatman Üniversitesi Mühendislik - Mimarlık Fakültesi Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.description.abstractA novel failure rate prediction model is developed by the extreme learning machine (ELM) to provide key information needed for optimum ongoing maintenance/rehabilitation of a water network, meaning the estimated times for the next failures of individual pipes within the network. The developed ELM model is trained using more than 9500 instances of pipe failure in the Greater Toronto Area, Canada from 1920 to 2005 with pipe attributes as inputs, including pipe length, diameter, material, and previously recorded failures. The models show recent, extensive usage of pipe coating with cement mortar and cathodic protection has significantly increased their lifespan. The predictive model includes the pipe protection method as pipe attributes and can reflect in its predictions, the effect of different pipe protection methods on the expected time to the next pipe failure. The developed ELM has a superior prediction accuracy relative to other available machine learning algorithms such as feed-forward artificial neural network that is trained by backpropagation, support vector regression, and non-linear regression. The utility of the models provides useful inputs when planning and budgeting for watermain inspection, maintenance, and rehabilitation.en_US
dc.identifier.citationSattar, A. M., Ertuğrul, Ö F., Gharabaghi, B., Mcbean, E. A., & Cao, J. (2017). Extreme learning machine model for water network management. Neural Computing and Applications, 31(1), pp. 157-169. https://doi.org/10.1007/s00521-017-2987-7en_US
dc.identifier.endpage169en_US
dc.identifier.issn0941-0643
dc.identifier.issn1433-3058
dc.identifier.issue1en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage157en_US
dc.identifier.urihttps://doi.org/10.1007/s00521-017-2987-7
dc.identifier.urihttps://hdl.handle.net/20.500.12402/2176
dc.identifier.volume31en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Natureen_US
dc.relation.isversionof10.1007/s00521-017-2987-7en_US
dc.relation.journalNeural Computing and Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.rightsAttribution-NonCommercial-ShareAlike 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/us/*
dc.subjectExtreme Machine Learningen_US
dc.subjectManagement Toolen_US
dc.subjectPipe Failureen_US
dc.subjectWater Pipe Networken_US
dc.titleExtreme learning machine model for water network managementen_US
dc.typeArticleen_US

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