THE APPLICATION OF THE RAYLEIGH PARAMETER IN MACHINE LEARNING PREDICTION OF WAVE HEIGHT
Waves directly affect maritime activities and navigation safety,and also contain enormous renewable energy,making it crucial to predict wave height,one of the core parameters of waves.This study is based on wave height data measured at the Xiaomai Island Station(36°N,120.6°E)in Shandong Province from July 2015 to June 2022.Three machine learning models,back-propagation neural network(BPNN),long short-term memory network(LSTM),and support vector machine regression(SVR),were used to predict wave heights,and the influence of Rayleigh parameter introduction into the prediction results was analyzed.The results show that the introduction of the Rayleigh parameter as one of the input features had limited improvement on the prediction of wave heights of 1 h and 6 h forecasts.The correlation between the predicted values and the test dataset was no more than 0.02,and the reduction in root mean square error(RMSE)did not exceed 0.01 m.In the 12 and 24 h predictions,the correlation between the BPNN and LSTM models was improved by 0.03～0.07,and the RMSE was decreased by 0.02～0.03 m,while the SVR model did not show significant changes in the prediction results.Therefore,the Rayleigh parameter involvement can help improve the mid-to long-term wave forecasting in the BPNN and LSTM models.In addition,the feature perturbation method(one of the calculation methods of feature importance in machine learning)has verified the importance of the Rayleigh parameter in wave height prediction.The introduction of the Rayleigh parameter provided a new approach for machine learning prediction of wave heights.
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