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dc.contributor.authorLiu, Jiao
dc.contributor.authorShi, Guoyou
dc.contributor.authorZhu, Kaige
dc.date.accessioned2021-12-15T11:23:07Z
dc.date.available2021-12-15T11:23:07Z
dc.date.issued2019
dc.identifier.citationLiu, J., Shi, G. and Zhu, K. (2019) High-Precision Combined Tidal Forecasting Model. Algorithms. 12(3), pp.65-81. DOI: https://doi.org/10.3390/a12030065en_US
dc.identifier.urihttps://repository.oceanbestpractices.org/handle/11329/1807
dc.description.abstractTo improve the overall accuracy of tidal forecasting and ameliorate the low accuracy of single harmonic analysis, this paper proposes a combined tidal forecasting model based on harmonic analysis and autoregressive integrated moving average–support vector regression (ARIMA-SVR). In tidal analysis, the resultant tide can be considered as a superposition of the astronomical tide level and the non-astronomical tidal level, which are affected by the tide-generating force and environmental factors, respectively. The tidal data are de-noised via wavelet analysis, and the astronomical tide level is subsequently calculated via harmonic analysis. The residual sequence generated via harmonic analysis is used as the sample dataset of the non-astronomical tidal level, and the tidal height of the system is calculated by the ARIMA-SVR model. Finally, the tidal values are predicted by linearly summing the calculated results of both systems. The simulation results were validated against the measured tidal data at the tidal station of Bay Waveland Yacht Club, USA. By considering the residual non-astronomical tide level effects (which are ignored in traditional harmonic analysis), the combined model improves the accuracy of tidal prediction. Moreover, the combined model is feasible and efficient.en_US
dc.description.sponsorshipNational Natural Science Foundation of China
dc.language.isoenen_US
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subject.otherTidal level prediction
dc.subject.otherCombined model
dc.subject.otherHarmonic analysis method
dc.subject.otherSupport Vector Regression (SVR)
dc.subject.otherAutoregressive Integrated Moving Average Model (ARIMA)
dc.titleHigh-Precision Combined Tidal Forecasting Model.en_US
dc.typeJournal Contributionen_US
dc.description.refereedRefereeden_US
dc.format.pagerangepp.65-81en_US
dc.identifier.doi10.3390/a12030065
dc.subject.parameterDisciplineSea levelen_US
dc.bibliographicCitation.titleAlgorithmsen_US
dc.bibliographicCitation.volume12en_US
dc.bibliographicCitation.issue3
dc.description.sdg14.aen_US
dc.description.eovSea surface heighten_US
dc.description.adoptionNovel (no adoption outside originators)en_US
dc.description.methodologyTypeMethoden_US
obps.contact.contactnameGuoyou Shi
obps.contact.contactemailnsgi@dlmu.edu.cn
obps.resourceurl.publisherhttps://www.mdpi.com/1999-4893/12/3/65


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Attribution 4.0 International
Except where otherwise noted, this item's license is described as Attribution 4.0 International