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dc.contributor.authorBrowne, Matthewen_US
dc.contributor.authorStrauss, Darrellen_US
dc.contributor.authorCastelle, Brunoen_US
dc.contributor.authorBlumenstein, Michaelen_US
dc.contributor.authorTomlinson, Rodgeren_US
dc.contributor.authorLane, Chrisen_US
dc.contributor.editorWilliam J. Emeryen_US
dc.date.accessioned2017-04-24T10:08:46Z
dc.date.available2017-04-24T10:08:46Z
dc.date.issued2006en_US
dc.date.modified2009-09-29T23:11:55Z
dc.identifier.issn1545598Xen_US
dc.identifier.doi10.1109/LGRS.2006.876225en_AU
dc.identifier.urihttp://hdl.handle.net/10072/14365
dc.description.abstractGlobal wind-wave models such as the National Oceanic and Atmospheric AdministrationWaveWatch 3 (NWW3) play an important role in monitoring the world's oceans. However, untransformed data at grid points in deep water provide a poor estimate of swell characteristics at nearshore locations, which are often of significant scientific, engineering, and public interest. Explicit wave modeling, such as the Simulating Waves Nearshore (SWAN), is one method for resolving the complex wave transformations affected by bathymetry, winds, and other local factors. However, obtaining accurate bathymetry and determining parameters for such models is often difficult. When target data is available (i.e., from in situ buoys or human observers, empirical alternatives such artificial neural networks (ANNs) and linear regression may be considered for inferring nearshore conditions from offshore model output. Using a sixfold cross-validation scheme, significant wave height Hs and period were estimated at one onshore and two nearshore locations. In estimating Hs at the shoreline, the validation performance of the best ANN was r = 0.91, as compared to those of linear regression (0.82), SWAN (0.78), and the NWW3 Hs baseline (0.54).en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.format.extent370497 bytes
dc.format.extent25070 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherInstitute of Electrical and Electronic Engineersen_US
dc.publisher.placeNew Yorken_US
dc.publisher.urihttp://ieeexplore.ieee.org/Xplore/dynhome.jspen_AU
dc.relation.ispartofstudentpublicationNen_AU
dc.relation.ispartofpagefrom462en_US
dc.relation.ispartofpageto466en_US
dc.relation.ispartofissue4en_US
dc.relation.ispartofjournalIEEE Geoscience and Remote Sensing Lettersen_US
dc.relation.ispartofvolume3en_US
dc.rights.retentionYen_AU
dc.subject.fieldofresearchcode260499en_US
dc.subject.fieldofresearchcode280212en_US
dc.titleEmpirical Estimation of Nearshore Waves From a Global Deep-Water Wave Modelen_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 2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_AU
gro.date.issued2006
gro.hasfulltextFull Text


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