Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/1952
Full metadata record
DC FieldValueLanguage
dc.contributor.authorTayfur, Gökmen-
dc.contributor.authorSingh, Vijay Pratap Ratap-
dc.date.accessioned2016-07-21T11:01:20Z
dc.date.available2016-07-21T11:01:20Z
dc.date.issued2005-11
dc.identifier.citationTayfur, G., and Singh, V.P. (2005). Predicting longitudinal dispersion coefficient in natural streams by artificial neural network. Journal of Hydraulic Engineering, 131(11). 991-1000. doi:10.1061/(ASCE)0733-9429(2005)131:11(991)en_US
dc.identifier.issn0733-9429
dc.identifier.issn0733-9429-
dc.identifier.urihttp://doi.org/10.1061/(ASCE)0733-9429(2005)131:11(991)
dc.identifier.urihttp://hdl.handle.net/11147/1952
dc.description.abstractAn-artificial neural network (ANN) model was developed to predict the longitudinal dispersion coefficient in natural streams and rivers. The hydraulic variables [flow discharge (Q), flow depth (H), flow velocity (U), shear velocity (u*), and relative shear velocity (U/ u*)] and geometric characteristics [channel width (B), channel sinuosity (σ), and channel shape parameter (β)] constituted inputs to the ANN model, whereas the dispersion coefficient (Kx) was the target model output. The model was trained and tested using 71 data sets of hydraulic and geometric parameters and dispersion coefficients measured on 29 streams and rivers in the United States. The training of the ANN model was accomplished with an explained variance of 90% of the dispersion coefficient. The dispersion coefficient values predicted by the ANN model satisfactorily compared with the measured values corresponding to different hydraulic and geometric characteristics. The predicted values were also compared with those predicted using several equations that have been suggested in the literature and it was found that the ANN model was superior in predicting the dispersion coefficient. The results of sensitivity analysis indicated that the Q data alone would be sufficient for predicting more frequently occurring low values of the dispersion coefficient (Kx < 100 m2/s). For narrower channels (B/H < 50) using only U/u* data would be sufficient to predict the coefficient. If β and σ were used along with the flow variables, the prediction capability of the ANN model would be significantly improved. Journal of Hydraulic Engineering.en_US
dc.language.isoenen_US
dc.publisherAmerican Society of Civil Engineers (ASCE)en_US
dc.relation.ispartofJournal of Hydraulic Engineeringen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCoefficientsen_US
dc.subjectNeural networksen_US
dc.subjectPhysical propertiesen_US
dc.subjectRiver flowen_US
dc.subjectSimulationen_US
dc.subjectStreamflowen_US
dc.titlePredicting longitudinal dispersion coefficient in natural streams by artificial neural networken_US
dc.typeArticleen_US
dc.authoridTR2054en_US
dc.institutionauthorTayfur, Gökmen-
dc.departmentİzmir Institute of Technology. Civil Engineeringen_US
dc.identifier.volume131en_US
dc.identifier.issue11en_US
dc.identifier.startpage991en_US
dc.identifier.endpage1000en_US
dc.identifier.wosWOS:000290174400002en_US
dc.identifier.scopus2-s2.0-27644468479en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.1061/(ASCE)0733-9429(2005)131:11(991)-
dc.relation.doi10.1061/(ASCE)0733-9429(2005)131:11(991)en_US
dc.coverage.doi10.1061/(ASCE)0733-9429(2005)131:11(991)en_US
dc.identifier.scopusqualityQ2-
item.openairetypeArticle-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextWith Fulltext-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.grantfulltextopen-
crisitem.author.dept03.03. Department 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 
1952.pdfMakale170.11 kBAdobe PDFThumbnail
View/Open
Show simple item record



CORE Recommender

SCOPUSTM   
Citations

93
checked on Mar 22, 2024

WEB OF SCIENCETM
Citations

27
checked on Mar 27, 2024

Page view(s)

170
checked on Mar 25, 2024

Download(s)

294
checked on Mar 25, 2024

Google ScholarTM

Check




Altmetric


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