Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/10487
Full metadata record
DC FieldValueLanguage
dc.contributor.authorTayfur, Gökmentr
dc.contributor.authorAksoy, Hafzullahtr
dc.contributor.authorEriş, Ebrutr
dc.date.accessioned2021-01-24T18:44:54Z-
dc.date.available2021-01-24T18:44:54Z-
dc.date.issued2020-
dc.identifier.issn1747-6585-
dc.identifier.issn1747-6593-
dc.identifier.urihttps://doi.org/10.1111/wej.12442-
dc.identifier.urihttps://hdl.handle.net/11147/10487-
dc.description.abstractBased on three rainfall run-off-induced sediment transport data for bare surface experimental plots, the generalized regression neural network (GRNN) and empirical models were developed to predict sediment load. Rainfall intensity, slope, rainfall duration, soil particle median diameter, clay content of the soil, rill density and soil particle mass density constituted the input variables of the models while sediment load was the target output. The GRNN model was trained and tested. The GRNN model was found successful in predicting sediment load. Sensitivity analysis by the GRNN model revealed that slope and rainfall duration were the most sensitive parameters. In addition to the GRNN model, two empirical models were proposed: (1) in the first empirical model, all the input variables were related to the sediment load, and (2) in the second empirical model, only rainfall intensity, slope and rainfall duration were related to the sediment load. The empirical models were calibrated and validated. At the calibration stage, the coefficients and the exponents of the empirical models were obtained using the genetic algorithm optimization method. The validated empirical models were also applied to two more experimental data sets: (1) one data set was from a field experiment, and (2) one set was from a laboratory experiment. The results indicated the success of the empirical models in predicting sediment load from bare land surfaces.en_US
dc.language.isoenen_US
dc.publisherWileyen_US
dc.relation.ispartofWater and Environment Journalen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectBare slopeen_US
dc.subjectEmpirical modelen_US
dc.subjectGenetic algorithmsen_US
dc.subjectGRNNen_US
dc.subjectSediment loaden_US
dc.titlePrediction of rainfall runoff-induced sediment load from bare land surfaces by generalized regression neural network and empirical modelen_US
dc.typeArticleen_US
dc.institutionauthorTayfur, Gökmentr
dc.departmentİzmir Institute of Technology. Civil Engineeringen_US
dc.identifier.volume34en_US
dc.identifier.issue1en_US
dc.identifier.startpage66en_US
dc.identifier.endpage76en_US
dc.identifier.wosWOS:000513489100015en_US
dc.identifier.scopus2-s2.0-85058015697en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.1111/wej.12442-
dc.relation.doi10.1111/wej.12442en_US
dc.coverage.doi10.1111/wej.12442en_US
dc.identifier.wosqualityQ2-
dc.identifier.scopusqualityQ3-
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 SizeFormat 
Water Environment J -2018.pdf467.34 kBAdobe PDFView/Open
Show simple item record



CORE Recommender

SCOPUSTM   
Citations

6
checked on Mar 22, 2024

WEB OF SCIENCETM
Citations

5
checked on Mar 16, 2024

Page view(s)

132
checked on Mar 25, 2024

Download(s)

48
checked on Mar 25, 2024

Google ScholarTM

Check




Altmetric


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