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dc.contributor.authorMandal, S.
dc.date.accessioned2009-01-07T11:01:37Z
dc.date.available2009-01-07T11:01:37Z
dc.date.issued2001
dc.identifier.citationInternational Conference in Ocean Engineering 2001, December 11-14, 2001, Silver Jubilee of the Department of Ocean Engineering. Proceedings, Vol.2; 499-504p.
dc.identifier.urihttp://drs.nio.org/drs/handle/2264/1606
dc.description.abstractPrediction of tides is very much essential for human activities and to reduce the construction cost in marine environment. This paper presents an application of the artificial neural network with back-propagation procedures for accurate prediction of tides. This neural network model predicts the time series data of hourly tides directly while using an efficient learning process called quickprop based on a previous set of data. Hourly tidal data measured at Gopalpur port - east coast of India was used for testing the back-propagation neural network model. Results show that the hourly data on tides for even a month can be predicted efficiently with a very high correlation coefficient (=0.9984).
dc.language.isoen
dc.publisherAllied, Chennai
dc.rightsCopyright [2001]. All efforts have been made to respect the copyright to the best of our knowledge. Inadvertent omissions, if brought to our notice, stand for correction and withdrawal of document from this repository.
dc.subjectocean tides
dc.subjecttidal prediction
dc.subjectoffshore structures
dc.subjectharmonic analysis
dc.subjecttidal models
dc.subjectcorrelation analysis
dc.titlePrediction of tides using back-propagation neural networks
dc.typeConference Article


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