Investigation of the nonlinear effect of measured wind speed in predicting the height of wind waves

Significant Wave Height (Hs) prediction is used in the analysis of marine systems including marine structural engineering and sediment transport. The Gulf of Mexico faces tropical storms shaped hurricane annually that affects the height of waves in this region, Therefore it is important to have precise estimation of the significant wave height. In this paper, we review previous studies and train artificial neural network model, to predict the effect of different wind speed powers and shear velocity in predicting the wave height in the next hours. The results showed that the presence of different wind speed powers increases the accuracy of predicting Hs relative to the wind shear speed. To increase the prediction accuracy, autocorrelation of the wave height data recorded in this region was used and a suitable model was presented. In this model was calculated power 2.3 of wind speed to predict the height of the next 3, 6 and 8 hours and power 1.9 to predict the height of the next 12 hours. Finally, the prediction results were compared with previous studies, and indicated the higher prediction accuracy.