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Hindcasting of storm waves using neural networks

DRS at CSIR-National Institute of Oceanography

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Title Hindcasting of storm waves using neural networks
 
Creator Rao, S.
Mandal, S.
 
Subject storms
cyclones
wind speed
surface water waves
wave hindcasting
coastal structures
offshore structures
wave heights
wave period
mathematical models
 
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 and 1979, are considered for analysis in this paper. The parametric hurricane wave prediction model by Young (1988) [Young, I.R., 1988. Parametric hurricane wave prediction model. Journal of Waterways Port Coastal and Ocean Engineering 114(5), 637-652] is used for hindcasting the wave heights and periods. Estimation of wave heights and periods is carried out using back propagation neural network with three updated algorithms, namely Rprop, Quickprop and superSAB. In neural network, the estimation is carried out using (1) difference between central and peripheral pressure, radius of maximum wind and speed of forward motion of cyclone as input nodes and the wave heights and periods as output nodes and (2) wind speed and fetch as input nodes and wave heights and periods as output nodes. The estimated values using neural networks match well with those estimated using Young's model and a high correlation is obtained namely (0.99).
 
Date 2008-02-22T04:58:47Z
2008-02-22T04:58:47Z
2005
 
Type Journal Article
 
Identifier Ocean engineering, Vol.32; 667-684p.
http://drs.nio.org/drs/handle/2264/906
 
Language en
 
Rights Copyright [2005]. It is tried to respect the rights of the copyright holders to the best of the knowledge. If it is brought to our notice by copyright holder that the rights are voilated then the item would be withdrawn.
 
Publisher Elsevier