Show simple item record

dc.contributor.authorNajafzadeh, Mohammad
dc.contributor.authorEtemad-Shahidi, Amir
dc.contributor.authorLim, Siow Yong
dc.date.accessioned2018-08-10T03:32:32Z
dc.date.available2018-08-10T03:32:32Z
dc.date.issued2016
dc.identifier.issn0029-8018
dc.identifier.doi10.1016/j.oceaneng.2015.10.053
dc.identifier.urihttp://hdl.handle.net/10072/142490
dc.description.abstractProtection of the channel bed in waterways against scour phenomena in long contractions is a very significant issue in channels design. Several field and experimental investigations were carried out to produce a relationship between the scour depth due to the contracted channels width and the governing variables. However, existing empirical equations do not always provide accurate scour prediction due to the complexity of the scour process. This paper investigates local scour depth in long contractions of rectangular channels using the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Support Vector Machines (SVM). For modeling of ANFIS and SVM, the input parameters that affect the scour phenomena are average flow velocity, critical threshold velocity of sediment movement, flow depth, median particle diameter, geometric standard deviation, un-contracted and contracted channel widths. Training and testing stages of the models are carried out using experimental data collected from different literature. The performances of the developed models are compared with those calculated using existing scour prediction equations. The results show that the developed ANFIS model can predict scour depth more accurately than SVM and the existing equations. A sensitivity analysis is also performed to determine the most important parameter in predicting the scour depth in long contractions.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherElsevier
dc.relation.ispartofpagefrom128
dc.relation.ispartofpageto135
dc.relation.ispartofjournalOcean Engineering
dc.relation.ispartofvolume111
dc.subject.fieldofresearchMaritime Engineering not elsewhere classified
dc.subject.fieldofresearchOceanography
dc.subject.fieldofresearchCivil Engineering
dc.subject.fieldofresearchMaritime Engineering
dc.subject.fieldofresearchcode091199
dc.subject.fieldofresearchcode0405
dc.subject.fieldofresearchcode0905
dc.subject.fieldofresearchcode0911
dc.titleScour prediction in long contractions using ANFIS and SVM
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.hasfulltextNo Full Text
gro.griffith.authorEtemad Shahidi, Amir F.


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

  • Journal articles
    Contains articles published by Griffith authors in scholarly journals.

Show simple item record