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dc.contributor.authorLee, TL
dc.contributor.authorTsai, CP
dc.contributor.authorJeng, DS
dc.contributor.authorShieh, RJ
dc.date.accessioned2017-05-03T11:58:50Z
dc.date.available2017-05-03T11:58:50Z
dc.date.issued2002
dc.identifier.issn0965-9978
dc.identifier.doi10.1016/S0965-9978(02)00043-1
dc.identifier.urihttp://hdl.handle.net/10072/6662
dc.description.abstractAccurate tidal prediction and supplement is an important task in determining constructions and human activities in coastal and oceanic areas. The harmonic tidal level is conventionally used to predict tide levels. However, determination of the tidal components using the spectral analysis requires a long-term tidal level record (more than one year [Handbook of coastal and ocean engineering 1 (1990) 534]). In addition, calculating the coefficients abbreviated of tide component using the least-squares method also requires a large database of tide measurements. This paper presents an application of the artificial neural network for predicting and supplementing the long-term tidal-level using the short term observed data. On site, tidal-level data at Taichung Harbor in Taiwan will be used to test the performance of the artificial neural network model. The results show that the tidal levels over a long duration can be efficiently predicted or supplemented using only a short-term tidal record
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.languageEnglish
dc.language.isoeng
dc.publisherElsevier
dc.publisher.placeOxford
dc.relation.ispartofpagefrom329
dc.relation.ispartofpageto338
dc.relation.ispartofissue6
dc.relation.ispartofjournalAdvances in Engineering Software
dc.relation.ispartofvolume33
dc.subject.fieldofresearchInformation and computing sciences
dc.subject.fieldofresearchEngineering
dc.subject.fieldofresearchcode46
dc.subject.fieldofresearchcode40
dc.titleNeural network for the prediction and supplement of tidal record in Taichung Habor, Taiwan
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.date.issued2015-02-02T04:17:13Z
gro.hasfulltextNo Full Text
gro.griffith.authorJeng, Dong-Sheng


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