Abstract
In the recent past, researchers and investigators in Civil Engineering have increasingly used soft computing tools to yield better results than the traditional numerical and statistical techniques in modelling water flows. Artificial Neural Network (ANN) is one such technique which has attained a strong foothold in the field of Hydrology and Ocean Engineering particularly for forecasting stream flows, wave forecasting, water level forecasting etc. In the last few years another efficient and useful soft computing tool, `Genetic Programming' (GP) has caught attention of the researchers for ocean engineering computations. GP is found to be a promising tool for prediction of oceanic parameters. This paper outlines the basic principles of GP Modelling and makes an attempt to estimate an important oceanic parameter - Significant Wave Height (SWH) using the wind information. Wave and wind measurements taken by moored ocean buoys are used to develop the GP models. The GP based estimations are found to have a reasonable accuracy in estimation of significant wave heights as evident from wave plots and accompanying high values of correlation coefficient.
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