Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/88127
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Civil and Environmental Engineering | - |
dc.creator | Shamshirband, S | - |
dc.creator | Mosavi, A | - |
dc.creator | Rabczuk, T | - |
dc.creator | Nabipour, N | - |
dc.creator | Chau, KW | - |
dc.date.accessioned | 2020-09-18T02:12:59Z | - |
dc.date.available | 2020-09-18T02:12:59Z | - |
dc.identifier.issn | 1994-2060 | - |
dc.identifier.uri | http://hdl.handle.net/10397/88127 | - |
dc.language.iso | en | en_US |
dc.publisher | Hong Kong Polytechnic University, Department of Civil and Structural Engineering | en_US |
dc.rights | © 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group | en_US |
dc.rights | This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. | en_US |
dc.rights | The following publication Shamshirband, S., Mosavi, A., Rabczuk, T., Nabipour, N, & Chau, K. W. (2020). Prediction of significant wave height; comparison between nested grid numerical model, and machine learning models of artificial neural networks, extreme learning and support vector machines. Engineering Applications of Computational Fluid Mechanics, 14(1), 805-817 is available at https://dx.doi.org/10.1080/19942060.2020.1773932 | en_US |
dc.subject | Numerical modeling | en_US |
dc.subject | Nested grid | en_US |
dc.subject | Wind waves | en_US |
dc.subject | Machine learning | en_US |
dc.subject | Extreme learning machines | en_US |
dc.subject | Deep learning | en_US |
dc.title | Prediction of significant wave height; comparison between nested grid numerical model, and machine learning models of artificial neural networks, extreme learning and support vector machines | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 805 | - |
dc.identifier.epage | 817 | - |
dc.identifier.volume | 14 | - |
dc.identifier.issue | 1 | - |
dc.identifier.doi | 10.1080/19942060.2020.1773932 | - |
dcterms.abstract | Estimation of wave height is essential for several coastal engineering applications. This study advances a nested grid numerical model and compare its efficiency with three machine learning (ML) methods of artificial neural networks (ANN), extreme learning machines (ELM) and support vector regression (SVR) for wave height modeling. The models are trained by surface wind data. The results demonstrate that all the models generally provide sound predictions. Due to the high level of variability in the bathymetry of the study area, implementation of the nested grid with different Whitecapping coefficient is a suitable approach to improve the efficiency of the numerical models. Performance on the ML models do not differ remarkably even though the ELM model slightly outperforms the other models. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Engineering applications of computational fluid mechanics, 2020, v. 14, no. 1, p. 805-817 | - |
dcterms.isPartOf | Engineering applications of computational fluid mechanics | - |
dcterms.issued | 2020 | - |
dc.identifier.isi | WOS:000544436100001 | - |
dc.identifier.scopus | 2-s2.0-85087401793 | - |
dc.identifier.eissn | 1997-003X | - |
dc.description.validate | 202009 bcrc | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_Scopus/WOS | en_US |
dc.description.pubStatus | Published | en_US |
dc.description.oaCategory | CC | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Shamshirband_Prediction_Significant_Wave.pdf | 2.7 MB | Adobe PDF | View/Open |
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