Wave forecasts using wind information and genetic programming

Forecasting of wave heights is essential for planning and operation of maritime activities. Traditionally, wave heights have been predicted using physics-based models, which rely primarily on the energy balance equation. More recently, soft computing techniques such as Artificial Neural Network (ANN), Genetic Programming (GP) have been used to generate forecasts with leads times from a few hours to several days. However, the forecast accuracy of both methods could be improved, particularly at peak wave heights, and at higher lead times. This paper forecasts the wave heights with lead times of 12 h and 24 h using GP. The data are obtained from two locations, along the North American and Indian coastlines. Wind information is used as an input. The modeling procedure relies heavily on the parameter kurtosis, or fourth moment. The forecasts are satisfactory, especially for the peak wave heights formed by the extreme events like hurricanes.

► Significant Wave Heights are predicted at 4 stations with 12 and 24 h lead time.
► Present and past wind data having high kurtosis for Hs is selected as inputs.
► Relatively new soft computing tool of Genetic Programming is used for the work.
► Wave plots and error measures indicate that the models have worked well.
► The forecasts are satisfactory including the peak wave heights also.