Predicting a water infrastructure leakage index via machine learning

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dc.authorid0000-0003-3696-9967
dc.contributor.authorKızılöz, Burak
dc.contributor.authorŞişman, Eyüp
dc.contributor.authorOruç, Halil Nurullah
dc.date.accessioned2022-03-21T12:48:04Z
dc.date.available2022-03-21T12:48:04Z
dc.date.issued2022
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.abstractIn this study, the infrastructure leakage index (ILI) indicator that is preferred frequently by the water utilities with sufficient data to determine the performances of water distribution systems is modeled for the first time through the three different methodologies using different input data. In addition to the variables in the literature used for the classical ILI calculations, the age parameter is also included in the models. In the first step, the ILI values have been estimated via multiple linear regression (MLR) using water supply quantity, water accrual quantity, network length, service connection length, number of service connections, and pressure variables. Secondly, the Artificial Neural Network (ANN) approach has been applied with raw data to improve the ILI prediction performance. Finally, the data set has been standardized with the Z-Score method for increasing the learning power of the ANN models, and then the ANN predictions have been made by converting the data through the principal component analysis (PCA) method to minimize complexity by reducing the data set size. The model predictions have been evaluated via mean square error, G-value, mean absolute error, mean bias error, and adjusted-R2 model performance scale. When the model outputs obtained at the end of the study are evaluated together with the classical ILI calculations, it is seen that the successful ILI predictions with three and four variables, including the age parameter, rather than six variables, have been made through the PC-ANN method. Water utilities with insufficient physical and operational data for ILI indicator calculation can make network performance evaluations by predicting the ILI through the models suggested in this study with high accuracy in a reliable way.
dc.identifier.citationKızılöz, B., Şişman, E. ve Oruç, H. N. (2022). Predicting a water infrastructure leakage index via machine learning. Utilities Policy, 75. https://doi.org/10.1016/j.jup.2022.101357
dc.identifier.doi10.1016/j.jup.2022.101357
dc.identifier.issn0957-1787
dc.identifier.scopus2-s2.0-85125727098
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.jup.2022.101357
dc.identifier.urihttps://hdl.handle.net/20.500.12511/9146
dc.identifier.volume75
dc.identifier.wos000783084300002en_US
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorŞişman, Eyüp
dc.language.isoen
dc.publisherElsevier Ltd
dc.relation.ispartofUtilities Policyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.subjectArtificial Neural Network
dc.subjectILI
dc.subjectInfrastructure Leakage Index
dc.subjectMachine Learning Solutions
dc.subjectMultiple Linear Regression
dc.subjectPrincipal Component Analysis
dc.titlePredicting a water infrastructure leakage index via machine learning
dc.typeArticle

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