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Multi-point tidal prediction using artificial neural network with tide-generating forces

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Title: Multi-point tidal prediction using artificial neural network with tide-generating forces
Authors: Chang, Hsien-Kuo hkc@faculty.nctu.edu.tw, Lin, Li-Ching1
Source: Coastal Engineering. Sep2006, Vol. 53 Issue 10, p857-864. 8p.
Subjects: Artificial neural networks, Forcing (Model theory), Ocean circulation, Artificial intelligence
Abstract: Abstract: This paper presents a neural network model of simulating tides at multi-points considering tide-generating forces. A comparison on the root mean square and correlation coefficient of three-year mixed tides at a single point computed with harmonic method, response–orthotide method, the NAO.99b model and the proposed model was made to show the prediction accuracy of each method. The proposed model is examined efficient as the harmonic method to estimate the tides at a single point. Extended application of the proposed model to predicting tides at some points neighboring to an original interest point identifies accurately simulating multi-point tides as the NAO.99b numerical model. [Copyright &y& Elsevier]
Copyright of Coastal Engineering is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 22219819
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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Items – Name: Title
  Label: Title
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  Data: Multi-point tidal prediction using artificial neural network with tide-generating forces
– Name: Author
  Label: Authors
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  Data: <searchLink fieldCode="AR" term="%22Chang%2C+Hsien-Kuo%22">Chang, Hsien-Kuo</searchLink><i> hkc@faculty.nctu.edu.tw</i><br /><searchLink fieldCode="AR" term="%22Lin%2C+Li-Ching%22">Lin, Li-Ching</searchLink><relatesTo>1</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Coastal+Engineering%22">Coastal Engineering</searchLink>. Sep2006, Vol. 53 Issue 10, p857-864. 8p.
– Name: Subject
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  Data: <searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Forcing+%28Model+theory%29%22">Forcing (Model theory)</searchLink><br /><searchLink fieldCode="DE" term="%22Ocean+circulation%22">Ocean circulation</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Abstract: This paper presents a neural network model of simulating tides at multi-points considering tide-generating forces. A comparison on the root mean square and correlation coefficient of three-year mixed tides at a single point computed with harmonic method, response–orthotide method, the NAO.99b model and the proposed model was made to show the prediction accuracy of each method. The proposed model is examined efficient as the harmonic method to estimate the tides at a single point. Extended application of the proposed model to predicting tides at some points neighboring to an original interest point identifies accurately simulating multi-point tides as the NAO.99b numerical model. [Copyright &y& Elsevier]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Coastal Engineering is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1016/j.coastaleng.2006.05.001
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 8
        StartPage: 857
    Subjects:
      – SubjectFull: Artificial neural networks
        Type: general
      – SubjectFull: Forcing (Model theory)
        Type: general
      – SubjectFull: Ocean circulation
        Type: general
      – SubjectFull: Artificial intelligence
        Type: general
    Titles:
      – TitleFull: Multi-point tidal prediction using artificial neural network with tide-generating forces
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            NameFull: Chang, Hsien-Kuo
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            NameFull: Lin, Li-Ching
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            – D: 01
              M: 09
              Text: Sep2006
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              Y: 2006
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              Value: 53
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              Value: 10
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            – TitleFull: Coastal Engineering
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