A hybrid prediction method for significant wave height based on deep learning
As an important oceanic environmental parameter,the accurate prediction of significant wave height(SWH)is of great significance in marine engineering.To address the issues such as deep learning easily falling into local optima,this paper proposes a hybrid SWH prediction method that combines depth and width.The method utilizes ensemble empirical mode decomposition to preprocess SWH,aiming to improve the common lag problem in deep learning prediction.Furthermore,it integrates the broad learning system with the long short-term memory network in deep learning to enhance prediction accuracy.The experimental results show that the hybrid SWH prediction method not only effectively improves the evaluation indicators such as root mean square error and mean absolute error,but also exhibits good robustness compared with existing methods.
万方数据
维普
