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Hindcasting cyclonic waves using neural networks

DRS at CSIR-National Institute of Oceanography

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Title Hindcasting cyclonic waves using neural networks
 
Creator Mandal, S.
Rao, S.
Chakravarty, N.V.
 
Subject ocean waves
cyclones
wave hindcasting
coastal structures
mathematical models
wave height
wave period
wave forecasting
correlation
 
Description 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).
 
Date 2008-08-02T11:43:24Z
2008-08-02T11:43:24Z
2002
 
Type Conference Article
 
Identifier 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.
http://drs.nio.org/drs/handle/2264/1332
 
Language en
 
Publisher KREC, Surathkal