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dc.contributor.authorMandal, S.
dc.contributor.authorPrabaharan, N.
dc.date.accessioned2008-08-02T11:37:55Z
dc.date.available2008-08-02T11:37:55Z
dc.date.issued2002
dc.identifier.citationConference on Hydraulics, Water Resources and Ocean Engineering. Hydro 2002, December 16-17, 2002. (Conf. on Hydraulics, Water Resources and Ocean Engineering. Hydro 2002; IIT, Bombay; India; 16-17 Dec 2002). Indian Society for Hydraulics; Pune; India; 2002; 260-263p.
dc.identifier.urihttp://drs.nio.org/drs/handle/2264/1331
dc.description.abstractThe tremendous increase in offshore operational activities demands improved wave forecasting techniques. With the knowledge of accurate wave conditions, it is possible to carry out the marine activities such as offshore drilling, naval operations, merchant vessel routing, nearshore construction, etc. more efficiently and safely. This paper presents an application of the Artificial Neural Network, namely Backpropagation Recurrent Neural Network (BRNN) with rprop update algorithm for wave forecasting. Measured ocean waves off Marmugao, west coast of India are used for this study. Here, the BRNN of 3, 6 and 12 hourly wave forecasting yields the correlation coefficients of 0.95, 0.90 and 0.87 respectively. This shows that the BRNN with rprop algorithm gives good results. Wave forecasting using recurrent network yields much better results than the previous neural network application
dc.language.isoen
dc.publisherIIT, Bombay
dc.subject:wave propatation
dc.subjectwave data
dc.subjectoffshore operations
dc.subjectwave height
dc.subjectsurface water waves
dc.titleRecurrent networks for wave forecasting
dc.typeConference Article


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