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dc.contributor.authorTayfur, Gökmen
dc.contributor.authorKarimi, Yashar
dc.contributor.authorSingh, Vijay P.
dc.date.accessioned2017-04-19T06:28:13Z
dc.date.available2017-04-19T06:28:13Z
dc.date.issued2013-05
dc.identifier.citationTayfur, G., Karimi, Y., and Singh, V.P. (2013). Principle component analysis in conjuction with data driven methods for sediment load prediction. Water Resources Management, 27(7), 2541-2554. doi:10.1007/s11269-013-0302-7en_US
dc.identifier.issn0920-4741
dc.identifier.urihttps://doi.org/10.1007/s11269-013-0302-7
dc.identifier.urihttp://hdl.handle.net/11147/5340
dc.description.abstractThis study investigates sediment load prediction and generalization from laboratory scale to field scale using principle component analysis (PCA) in conjunction with data driven methods of artificial neural networks (ANNs) and genetic algorithms (GAs). Five main dimensionless parameters for total load are identified by using PCA. These parameters are used in the input vector of ANN for predicting total sediment loads. In addition, nonlinear equations are constructed, based upon the same identified dimensionless parameters. The optimal values of exponents and constants of the equations are obtained by the GA method. The performance of the so-developed ANN and GA based methods is evaluated using laboratory and field data. Results show that the expert methods (ANN and GA), calibrated with laboratory data, are capable of predicting total sediment load in field, thus showing their transferability. In addition, this study shows that the expert methods are not transferable for suspended load, perhaps due to insufficient laboratory data. Yet, these methods are able to predict suspended load in field, when trained with respective field data.en_US
dc.language.isoengen_US
dc.publisherSpringeren_US
dc.relation.isversionof10.1007/s11269-013-0302-7en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectPrinciple component analysisen_US
dc.subjectSediment loaden_US
dc.subjectArtificial neural networken_US
dc.subjectGenetic algorithmsen_US
dc.subjectTransferabilityen_US
dc.titlePrinciple component analysis in conjuction with data driven methods for sediment load predictionen_US
dc.typearticleen_US
dc.contributor.authorIDTR2054en_US
dc.contributor.institutionauthorTayfur, Gökmen
dc.relation.journalWater Resources Managementen_US
dc.contributor.departmentİYTE, Mühendislik Fakültesi, İnşaat Mühendisliği Bölümüen_US
dc.identifier.volume27en_US
dc.identifier.issue7en_US
dc.identifier.startpage2541en_US
dc.identifier.endpage2554en_US
dc.identifier.wosWOS:000318004800039
dc.identifier.scopusSCOPUS:2-s2.0-84876429805
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US


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