Research on deep learning models for predicting significant wave height
Based on deep learning models RNN,LSTM,and GRU,this study aims to improve the predictive performance of the model for 44013,44014,and 44017 buoys in the NOAA buoy dataset through Spearman correlation analysis.The experimental results show that after conducting correlation analysis,the prediction performance of S-RNN,S-LSTM,and S-GRU models is better than that of the original RNN,LSTM,and GRU models.In addition,an LSTM Attention wave height prediction model based on LSTM was proposed and relevant experiments were conducted to quantify the predictive performance of the LSTM Attention model.The experimental results showed that the LSTM Attention model had better predictive performance.To evaluate the generalization ability of the model,a learning method using neighboring buoy data was proposed to predict missing data buoys.The experimental results show that the prediction accuracy of this method can reach 97.93%.This study provides new methods and ideas for wave prediction,and also provides reference for the application of deep learning models in wave prediction in the future.
deep learningsea wavesignificant wave heightLSTM-Attention
NETL
NSTL
万方数据
