Hindcasting cyclonic waves using neural networks
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).
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