Estimation of Failure Rate in Water Distribution Network Using Fuzzy Clustering and LS-SVM Methods

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dc.authoridFIRAT, MAHMUT/0000-0002-8010-9289
dc.authoridAYDOGDU, MAHMUT/0000-0002-7339-2442
dc.authorwosidFIRAT, MAHMUT/ABG-7962-2020
dc.authorwosidAYDOĞDU, MAHMUT/AAJ-5483-2020
dc.authorwosidAYDOGDU, MAHMUT/AAG-6802-2021
dc.contributor.authorAydogdu, Mahmut
dc.contributor.authorFirat, Mahmut
dc.date.accessioned2024-08-04T20:40:08Z
dc.date.available2024-08-04T20:40:08Z
dc.date.issued2015
dc.departmentİnönü Üniversitesien_US
dc.description.abstractIn this study, a novel approach combining fuzzy clustering and Least Squares Support Vector machine (LS-SVM) methods is developed for estimation of failure rate in water distribution networks and for determination of the relationship between failure rate-effective factors. For this aim, failure data observed Malatya water distribution network during 2006-2012 was selected as study area. In first phase, estimation model was developed and tested for the complete data set in estimating the failure rate by LS-SVM method. Then, in order to develop a more sensitive estimation model and to improve the performance of LS-SVM, 9 sub-regions were defined with similar characteristics by using fuzzy clustering method. Then failure rate estimation was carried out for each of the sub-regions using by LS-SVM method. Feed Forward Neural Network (FFNN) and Generalized Regression Neural Network (GRNN) methods were also used for estimation of failure rate and the results were compared with those of LS-SVM. The criteria such as Correlation Coefficient (R), Efficieny (E) and Root Mean Square Error (RMSE) were used to evaluate the performance of models. The results showed that LS-SVM model gives better results in comparison with the FFNN and GRNN models. It was also determined that LSSVM model results for the sub-regions defined by clustering analysis are better and that the clustering analysis increases the estimation model performance in addition to the fact that the estimation results have become better. In conclusion, it can be possible to develop a more sensitive estimation models using fuzzy clustering and LSSVM methods.en_US
dc.identifier.doi10.1007/s11269-014-0895-5
dc.identifier.endpage1590en_US
dc.identifier.issn0920-4741
dc.identifier.issn1573-1650
dc.identifier.issue5en_US
dc.identifier.scopus2-s2.0-84925489855en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1575en_US
dc.identifier.urihttps://doi.org/10.1007/s11269-014-0895-5
dc.identifier.urihttps://hdl.handle.net/11616/96720
dc.identifier.volume29en_US
dc.identifier.wosWOS:000349361000012en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofWater Resources Managementen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectWater distribution networken_US
dc.subjectFailure rateen_US
dc.subjectLS-SVMen_US
dc.subjectFuzzy clusteringen_US
dc.titleEstimation of Failure Rate in Water Distribution Network Using Fuzzy Clustering and LS-SVM Methodsen_US
dc.typeArticleen_US

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