Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/5340
Full metadata record
DC FieldValueLanguage
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.issn1573-1650-
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.isoenen_US
dc.publisherSpringer Verlagen_US
dc.relation.ispartofWater Resources Managementen_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.authoridTR2054en_US
dc.contributor.departmentIzmir Institute of Technology. Civil Engineeringen_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
dc.identifier.doi10.1007/s11269-013-0302-7-
dc.relation.doi10.1007/s11269-013-0302-7en_US
dc.coverage.doi10.1007/s11269-013-0302-7en_US
item.openairetypeArticle-
item.fulltextWith Fulltext-
item.grantfulltextopen-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
item.languageiso639-1en-
crisitem.author.deptDepartment of Civil Engineering-
Appears in Collections:Civil Engineering / İnşaat Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Files in This Item:
File Description SizeFormat 
5340.pdfMakale374.52 kBAdobe PDFThumbnail
View/Open
Show simple item record

CORE Recommender

SCOPUSTM   
Citations

17
checked on Sep 25, 2021

WEB OF SCIENCETM
Citations

19
checked on Sep 25, 2021

Page view(s)

20
checked on Sep 28, 2021

Download(s)

18
checked on Sep 28, 2021

Google ScholarTM

Check

Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.