Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.advwatres.2004.03.006
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dc.titleEnhancing tidal prediction accuracy in a deterministic model using chaos theory
dc.contributor.authorSannasiraj, S.A.
dc.contributor.authorZhang, H.
dc.contributor.authorBabovic, V.
dc.contributor.authorChan, E.S.
dc.date.accessioned2014-06-17T08:17:52Z
dc.date.available2014-06-17T08:17:52Z
dc.date.issued2004-07
dc.identifier.citationSannasiraj, S.A., Zhang, H., Babovic, V., Chan, E.S. (2004-07). Enhancing tidal prediction accuracy in a deterministic model using chaos theory. Advances in Water Resources 27 (7) : 761-772. ScholarBank@NUS Repository. https://doi.org/10.1016/j.advwatres.2004.03.006
dc.identifier.issn03091708
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/65537
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. © 2004 Elsevier Ltd. All rights reserved.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/j.advwatres.2004.03.006
dc.sourceScopus
dc.subjectEmbedding theorem
dc.subjectGenetic algorithm
dc.subjectLocal model
dc.subjectTidal forecasting
dc.subjectTime delay
dc.typeArticle
dc.contributor.departmentCIVIL ENGINEERING
dc.description.doi10.1016/j.advwatres.2004.03.006
dc.description.sourcetitleAdvances in Water Resources
dc.description.volume27
dc.description.issue7
dc.description.page761-772
dc.description.codenAWRED
dc.identifier.isiut000222810600007
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