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dc.contributor.authorSannasiraj, S.en_US
dc.contributor.authorZhang, Hongen_US
dc.contributor.authorBabovic, Vladanen_US
dc.contributor.authorChan, Eng Soonen_US
dc.contributor.editorCasey T Miller, D.A. Barry, Marc Parlangeen_US
dc.date.accessioned2017-05-03T14:30:33Z
dc.date.available2017-05-03T14:30:33Z
dc.date.issued2004en_US
dc.identifier.issn03091708en_US
dc.identifier.doi10.1016/j.advwatres.2004.03.006en_US
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.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_US
dc.languageEnglishen_US
dc.language.isoen_US
dc.publisherElsevier Scienceen_US
dc.publisher.placeUnited Kingdomen_US
dc.relation.ispartofpagefrom761en_US
dc.relation.ispartofpageto772en_US
dc.relation.ispartofjournalAdvances in Water Resourcesen_US
dc.relation.ispartofvolume27en_US
dc.subject.fieldofresearchcode280212en_US
dc.subject.fieldofresearchcode291205en_US
dc.titleEnhancing tidal prediction accuracy in a deterministic model using chaos theoryen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Peer Reviewed (HERDC)en_US
dc.type.codeC - Journal Articlesen_US
gro.date.issued2015-02-02T04:15:54Z
gro.hasfulltextNo Full Text


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