Show simple item record

dc.contributor.authorBrowne, Matthewen_US
dc.contributor.authorCastelle, Brunoen_US
dc.contributor.authorStrauss, Darrellen_US
dc.contributor.authorTomlinson, Rodgeren_US
dc.contributor.authorBlumenstein, Michaelen_US
dc.contributor.authorLane, Chrisen_US
dc.contributor.editorH F Burcharthen_US
dc.date.accessioned2017-05-03T12:10:45Z
dc.date.available2017-05-03T12:10:45Z
dc.date.issued2007en_US
dc.date.modified2009-08-27T06:53:17Z
dc.identifier.issn03783839en_US
dc.identifier.doi10.1016/j.coastaleng.2006.11.007en_AU
dc.identifier.urihttp://hdl.handle.net/10072/17987
dc.description.abstractEstimation of swell conditions in coastal regions is important for a variety of public, government, and research applications. Driving a model of the near-shore wave transformation from an offshore global swell model such as NOAAWaveWatch3 is an economical means to arrive at swell size estimates at particular locations of interest. Recently, some work (e.g. Browne et al. [Browne, M., Strauss, D., Castelle, B., Blumenstein, M., Tomlinson, R., 2006. Local swell estimation and prediction from a global wind-wave model. IEEE Geoscience and Remote Sensing Letters 3 (4), 462-466.]) has examined an artificial neural network (ANN) based, empirical approach to wave estimation. Here, we provide a comprehensive evaluation of two data driven approaches to estimating waves near-shore (linear and ANN), and also contrast these with a more traditional spectral wave simulation model (SWAN). Performance was assessed on data gathered from a total of 17 near-shore locations, with heterogenous geography and bathymetry, around the continent of Australia over a 7 month period. It was found that the ANNs out-performed SWAN and the non-linear architecture consistently out-performed the linear method. Variability in performance and differential performance with regard to geographical location could largely be explained in terms of the underlying complexity of the local wave transformation.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.format.extent2143181 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherElsevier BVen_US
dc.publisher.placeNetherlandsen_US
dc.publisher.urihttp://www.elsevier.com/locate/coastalengen_AU
dc.relation.ispartofstudentpublicationNen_AU
dc.relation.ispartofpagefrom445en_US
dc.relation.ispartofpageto460en_US
dc.relation.ispartofissue5en_US
dc.relation.ispartofjournalCoastal Engineeringen_US
dc.relation.ispartofvolume54en_US
dc.rights.retentionYen_AU
dc.subject.fieldofresearchcode291101en_US
dc.titleNear-shore swell estimation from a global wind-wave model: Spectral process, linear, and artificial neural network modelsen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Peer Reviewed (HERDC)en_US
dc.type.codeC - Journal Articlesen_US
gro.facultyGriffith Sciences, Griffith School of Environmenten_US
gro.rights.copyrightCopyright 2007 Elsevier. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. Please refer to the journal's website for access to the definitive, published version.en_AU
gro.date.issued2007
gro.hasfulltextFull Text


Files in 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