Prediction of offshore bar-shape parameters resulted by cross-shore sediment transport using neural network

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dc.authoridOzolcer, Ismail Hakki -- 0000-0002-8404-0522
dc.contributor.authorKömürcü, Murat İhsan
dc.contributor.authorKömür, Mehmet Aydın
dc.contributor.authorAkpınar, Adem
dc.contributor.authorÖzölçer, İsmail Hakkı
dc.contributor.authorYüksek, Ömer
dc.date.accessioned13.07.201910:50:10
dc.date.accessioned2019-07-29T19:29:03Z
dc.date.available13.07.201910:50:10
dc.date.available2019-07-29T19:29:03Z
dc.date.issued2013
dc.departmentMühendislik Fakültesi
dc.description.abstractIn order to understand the features of coastal zone and to utilize the coastal areas, it is necessary to determine the sediment movement and the resulting transport. Waves, topographic features, and material properties are known as the most important factors affecting the sediment movement and coastal profiles. In this study, by taking into consideration of wave height and period (H-0, T), bed slope (m) and sediment diameter (d(50)), cross-shore sediment movement was studied in a physical model and various bar-shape parameters of the resultant erosion type profile were determined. Using 80 experimental data which are obtained from physical model studies, a neural network (NN) has been calibrated to predict bar-shape parameters of beach profiles. A sensitivity analysis was firstly carried out to decide data of training and test sets. Four different models, in which the rates of their training and testing set data were 80% and 20%, 70% and 30%, 60% and 40%, 50% and 50% were constituted and their performances were compared. It was determined that the model, in which the rate of its training and testing set data was 80% and 20%, respectively, has the best results. Therefore, a total of 64 experimental data were used as training set and the remainders of the experimental data were used as a testing set for the model. The performance of the NN model was compared with the regression equations developed in a previous study and the equations cited in literature indicating better performance over the equations. (c) 2013 Elsevier Ltd. All rights reserved.
dc.identifier.doi10.1016/j.apor.2013.01.003
dc.identifier.endpage82en_US
dc.identifier.issn0141-1187
dc.identifier.scopusqualityQ1
dc.identifier.startpage74en_US
dc.identifier.urihttps://doi.org/10.1016/j.apor.2013.01.003
dc.identifier.urihttps://hdl.handle.net/20.500.12451/6121
dc.identifier.volume40en_US
dc.identifier.wosWOS:000316533800008
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMühendislik Fakültesi
dc.relation.ispartofApplied Ocean Research
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectCoastal Profiles
dc.subjectNeural Network
dc.subjectStorm-built Profiles
dc.subjectBar Characteristics
dc.subjectExperimental Study
dc.titlePrediction of offshore bar-shape parameters resulted by cross-shore sediment transport using neural network
dc.typeArticle

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