Hindcasting cyclonic waves using neural networks

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dc.contributor.author Mandal, S.
dc.contributor.author Rao, S.
dc.contributor.author Chakravarty, N.V.
dc.date.accessioned 2008-08-02T11:43:24Z
dc.date.available 2008-08-02T11:43:24Z
dc.date.issued 2002
dc.identifier.citation Proceedings of International Conference on Ship and Ocean Technology (SHOT)-2002. 18-20 December 2002. (Int. Conf. on Ship and Ocean Technology (SHOT)-2002; 18-20 Dec 2002). ; 2002; 9 pp.
dc.identifier.uri http://drs.nio.org/drs/handle/2264/1332
dc.description.abstract Cyclone generated waves play a significant role in the design of coastal and offshore structures. Instead of conventional numerical models, neural network approach is used in the present study to estimate the wave parameters from cyclone generated wind fields. Eleven cyclones, which crossed the southern east coast of India between 1962 to 1979 are considered for analysis in this paper. The parametric hurricane wave prediction model by Young (1988) is used for hindcasting the wave heights and periods. Estimation of wave heights and periods is carried out using backpropagation neural network with three updated algorithms, namely Rprop, Quickprop and superSAB. In neural network the difference between central and peripheral pressure, radius of maximum wind and speed of forward motion of cyclone are given as input nodes and the wave heights and periods are output nodes. The estimated values using neural networks match well with those estimated using Young's model and a highest correlation is obtained (0.99).
dc.language.iso en
dc.publisher KREC, Surathkal
dc.subject ocean waves
dc.subject cyclones
dc.subject wave hindcasting
dc.subject coastal structures
dc.subject mathematical models
dc.subject wave height
dc.subject wave period
dc.subject wave forecasting
dc.subject correlation
dc.title Hindcasting cyclonic waves using neural networks
dc.type Conference Article

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