A Modular Real-time Tidal Prediction Model based on Grey-GMDH Neural Network.
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| Title: | A Modular Real-time Tidal Prediction Model based on Grey-GMDH Neural Network. |
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| Authors: | Zhang, Ze-Guo1, Yin, Jian-Chuan1 yinjianchuan@dlmu.edu.cn, Liu, Cheng1 |
| Source: | Applied Artificial Intelligence. 2018, Vol. 32 Issue 2, p165-185. 21p. |
| Subjects: | GMDH algorithms, Prediction models, Artificial neural networks, Coastal engineering, Marine engineering |
| Abstract: | Real-time prediction of tidal level is of great significance for activities of human beings in the fields of marine and coastal engineering. However, the disturbance factors of tidal level are very intricate, which deteriorate the tidal prediction accuracy. To improve the accuracy of real-time tidal-level prediction, a modular real-time tidal-level prediction approach is proposed based on the grey group method of data handling (Grey-GMDH) neural network. The modular model is composed of astronomical tide parts caused by celestial bodies’ movement and the nonastronomical tide parts caused by various meteorological and other environmental factors. The GMDH is a polynomial network that is commonly used in prediction and pattern recognition. However, GMDH is sensitive to nondeterministic time series, which would result in low accuracy of prediction. In this study, the grey prediction theory is introduced into the GMDH prediction model to alleviate the unfavorable effects of uncertainty caused by various environmental factors and the adverse effects caused thereby on the prediction accuracy. In this study of tidal prediction, the Grey-GMDH model is used to predict the nonastronomical tide parts, whereas the conventional harmonic analysis model is used to predict the astronomical tide parts. The final prediction result is achieved by combining the estimation outputs of the harmonious analysis model and the Grey-GMDH model. Measured tidal-level data of San Diego tidal station is selected as the testing database. Simulation and experimental results confirm that the proposed approach can achieve real-time predictions for tidal level with high accuracy, satisfactory convergence and stability. [ABSTRACT FROM AUTHOR] |
| Copyright of Applied Artificial Intelligence is the property of Taylor & Francis Ltd 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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| Header | DbId: egs DbLabel: Engineering Source An: 128967995 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Modular Real-time Tidal Prediction Model based on Grey-GMDH Neural Network. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zhang%2C+Ze-Guo%22">Zhang, Ze-Guo</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Yin%2C+Jian-Chuan%22">Yin, Jian-Chuan</searchLink><relatesTo>1</relatesTo><i> yinjianchuan@dlmu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Liu%2C+Cheng%22">Liu, Cheng</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Applied+Artificial+Intelligence%22">Applied Artificial Intelligence</searchLink>. 2018, Vol. 32 Issue 2, p165-185. 21p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22GMDH+algorithms%22">GMDH algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Coastal+engineering%22">Coastal engineering</searchLink><br /><searchLink fieldCode="DE" term="%22Marine+engineering%22">Marine engineering</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Real-time prediction of tidal level is of great significance for activities of human beings in the fields of marine and coastal engineering. However, the disturbance factors of tidal level are very intricate, which deteriorate the tidal prediction accuracy. To improve the accuracy of real-time tidal-level prediction, a modular real-time tidal-level prediction approach is proposed based on the grey group method of data handling (Grey-GMDH) neural network. The modular model is composed of astronomical tide parts caused by celestial bodies’ movement and the nonastronomical tide parts caused by various meteorological and other environmental factors. The GMDH is a polynomial network that is commonly used in prediction and pattern recognition. However, GMDH is sensitive to nondeterministic time series, which would result in low accuracy of prediction. In this study, the grey prediction theory is introduced into the GMDH prediction model to alleviate the unfavorable effects of uncertainty caused by various environmental factors and the adverse effects caused thereby on the prediction accuracy. In this study of tidal prediction, the Grey-GMDH model is used to predict the nonastronomical tide parts, whereas the conventional harmonic analysis model is used to predict the astronomical tide parts. The final prediction result is achieved by combining the estimation outputs of the harmonious analysis model and the Grey-GMDH model. Measured tidal-level data of San Diego tidal station is selected as the testing database. Simulation and experimental results confirm that the proposed approach can achieve real-time predictions for tidal level with high accuracy, satisfactory convergence and stability. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Applied Artificial Intelligence is the property of Taylor & Francis Ltd 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: BibEntity: Identifiers: – Type: doi Value: 10.1080/08839514.2018.1451220 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 21 StartPage: 165 Subjects: – SubjectFull: GMDH algorithms Type: general – SubjectFull: Prediction models Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Coastal engineering Type: general – SubjectFull: Marine engineering Type: general Titles: – TitleFull: A Modular Real-time Tidal Prediction Model based on Grey-GMDH Neural Network. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhang, Ze-Guo – PersonEntity: Name: NameFull: Yin, Jian-Chuan – PersonEntity: Name: NameFull: Liu, Cheng IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: 2018 Type: published Y: 2018 Identifiers: – Type: issn-print Value: 08839514 Numbering: – Type: volume Value: 32 – Type: issue Value: 2 Titles: – TitleFull: Applied Artificial Intelligence Type: main |
| ResultId | 1 |
