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dc.contributor.authorSannasiraj, SA
dc.contributor.authorZhang, H
dc.contributor.authorBabovic, V
dc.contributor.authorChan, ES
dc.contributor.editorCasey T Miller, D.A. Barry, Marc Parlange
dc.date.accessioned2017-05-03T14:30:33Z
dc.date.available2017-05-03T14:30:33Z
dc.date.issued2004
dc.identifier.issn0309-1708
dc.identifier.doi10.1016/j.advwatres.2004.03.006
dc.identifier.urihttp://hdl.handle.net/10072/5173
dc.description.abstractThe classical deterministic approach to tidal prediction is based on barotropic or baroclinic models with prescribed boundary conditions from a global model or measurements. The prediction by the deterministic model is limited by the precision of the prescribed initial and boundary conditions. Improvement to the knowledge of model formulation would only marginally increase the prediction accuracy without the correct driving forces. This study describes an improvement in the forecasting capability of the tidal model by combining the best of a deterministic model and a stochastic model. The latter is overlaid on the numerical model predictions to improve the forecast accuracy. The tidal prediction is carried out using a three-dimensional baroclinic model and, error correction is instigated using a stochastic model based on a local linear approximation. Embedding theorem based on the time lagged embedded vectors is the basis for the stochastic model. The combined model could achieve an efficiency of 80% for 1 day tidal forecast and 73% for a 7 day tidal forecast as compared to the deterministic model estimation.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.languageEnglish
dc.language.isoeng
dc.publisherElsevier Science
dc.publisher.placeUnited Kingdom
dc.relation.ispartofpagefrom761
dc.relation.ispartofpageto772
dc.relation.ispartofjournalAdvances in Water Resources
dc.relation.ispartofvolume27
dc.subject.fieldofresearchApplied mathematics
dc.subject.fieldofresearchCivil engineering
dc.subject.fieldofresearchEnvironmental engineering
dc.subject.fieldofresearchcode4901
dc.subject.fieldofresearchcode4005
dc.subject.fieldofresearchcode4011
dc.titleEnhancing tidal prediction accuracy in a deterministic model using chaos theory
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.date.issued2015-02-02T04:15:54Z
gro.hasfulltextNo Full Text
gro.griffith.authorZhang, Hong


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