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dc.contributor.authorHosseinzadeh, Shabnam
dc.contributor.authorEtemad-Shahidi, Amir
dc.contributor.authorKoosheh, Ali
dc.date.accessioned2022-02-09T01:34:28Z
dc.date.available2022-02-09T01:34:28Z
dc.date.issued2021
dc.identifier.issn1464-7141
dc.identifier.doi10.2166/hydro.2021.046
dc.identifier.urihttp://hdl.handle.net/10072/412174
dc.description.abstractThe accurate prediction of the mean wave overtopping rate at breakwaters is vital for a safe design. Hence, providing a robust tool as a preliminary estimator can be useful for practitioners. Recently, soft computing tools such as artificial neural networks (ANN) have been developed as alternatives to traditional overtopping formulae. The goal of this paper is to assess the capabilities of two kernel-based methods, namely Gaussian process regression (GPR) and support vector regression for the prediction of mean wave overtopping rate at sloped breakwaters. An extensive dataset taken from the EurOtop database, including rubble mound structures with permeable core, straight slopes, without berm, and crown wall, was employed to develop the models. Different combinations of the important dimensionless parameters representing structural features and wave conditions were tested based on the sensitivity analysis for developing the models. The obtained results were compared with those of the ANN model and the existing empirical formulae. The modified Taylor diagram was used to compare the models graphically. The results showed the superiority of kernel-based models, especially the GPR model over the ANN model and empirical formulae. In addition, the optimal input combination was introduced based on accuracy and the number of input parameters criteria. Finally, the physical consistencies of developed models were investigated, the results of which demonstrated the reliability of kernel-based models in terms of delivering physics of overtopping phenomenon.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherIWA Publishing
dc.relation.ispartofpagefrom1030
dc.relation.ispartofpageto1049
dc.relation.ispartofissue5
dc.relation.ispartofjournalJournal of Hydroinformatics
dc.relation.ispartofvolume23
dc.subject.fieldofresearchCivil engineering
dc.subject.fieldofresearchOcean engineering
dc.subject.fieldofresearchcode4005
dc.subject.fieldofresearchcode401503
dc.subject.keywordsScience & Technology
dc.subject.keywordsLife Sciences & Biomedicine
dc.subject.keywordsPhysical Sciences
dc.subject.keywordsComputer Science, Interdisciplinary Applications
dc.titlePrediction of mean wave overtopping at simple sloped breakwaters using kernel-based methods
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationHosseinzadeh, S; Etemad-Shahidi, A; Koosheh, A, Prediction of mean wave overtopping at simple sloped breakwaters using kernel-based methods, Journal of Hydroinformatics, 2021, 23 (5), pp. 1030-1049
dcterms.licensehttps://creativecommons.org/licenses/by/4.0/
dc.date.updated2022-02-09T01:32:16Z
dc.description.versionVersion of Record (VoR)
gro.rights.copyright© 2021 The Authors. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/).
gro.hasfulltextFull Text
gro.griffith.authorEtemad Shahidi, Amir F.


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