Artificial neural network system analysis and Kriging methodology for estimation of non-revenue water ratio

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dc.authorid0000-0003-3696-9967
dc.contributor.authorŞişman, Eyüp
dc.contributor.authorKızılöz, Burak
dc.date.accessioned2020-12-21T07:52:32Z
dc.date.available2020-12-21T07:52:32Z
dc.date.issued2020
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, İnşaat Mühendisliği Bölümü
dc.departmentİstanbul Medipol Üniversitesi, Rektörlük, İklim Değişikliği Araştırmaları Araştırma Merkezi (İKLİMER)
dc.description.abstractThe non-revenue water (NRW) ratio parameter is significantly important for performance evaluation of water distribution systems. In order to evaluate the NRW ratio, the variables influencing this parameter should be determined. Therefore, the first aim of the paper is to define the variables which are influential on the estimation of the NRW ratio and then analyze these variables by using artificial neural networks (ANNs) methodology by means of 50 models with one, two, three, and four-variable input. Secondly, in this study, the NRW ratios have been predicted for the first time by using the Kriging methodology through only two variables. By using the data measured in 12 district meter areas (DMA) in Kocaeli, 60 models in total have been established for NRW ratio prediction through the ANN and Kriging methodologies. The ANN models are closed-box models and therefore the interpretation of the ANN model results requires higher expert opinion. As a consequence, the results show that Kriging model graphs produce much more useful information than ANN models in terms of application and interpretation.
dc.identifier.citationŞişman, E. ve Kızılöz, B. (2020). Artificial neural network system analysis and Kriging methodology for estimation of non-revenue water ratio. Water Science and Technology: Water Supply, 20(5), 1871-1883. https://dx.doi.org/10.2166/ws.2020.095
dc.identifier.doi10.2166/ws.2020.095
dc.identifier.endpage1883
dc.identifier.issn1606-9749
dc.identifier.issn1607-0798
dc.identifier.issue5
dc.identifier.scopusqualityQ3
dc.identifier.startpage1871
dc.identifier.urihttps://dx.doi.org/10.2166/ws.2020.095
dc.identifier.urihttps://hdl.handle.net/20.500.12511/6108
dc.identifier.volume20
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherIWA Publishing
dc.relation.ispartofWater Science and Technology: Water Supplyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.subjectANNs
dc.subjectDMA
dc.subjectKriging
dc.subjectNRW Ratio
dc.subjectWater Distribution Systems
dc.titleArtificial neural network system analysis and Kriging methodology for estimation of non-revenue water ratio
dc.typeArticle

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