Fusing MIC and Res-LSTM models for significant wave height prediction
The prediction of significant wave height(SWH)plays an important role in marine transportation and maritime activities.Based on the near-shore real measurement data of the Hailing Island,Yangjiang,China,a network model integrating the maximum information coefficient algorithm(MIC),residual network(ResNet)and long and short-term memory network(LSTM)is proposed.Firstly,the MIC algorithm was used to screen out the parameters with high correlation with the target predictors from the dataset as the input of the model.Then the residual network was introduced into the LSTM to construct the Res-LSTM prediction model.Finally,the r-squared(R2),root mean square error(RMSE),mean absolute error(MAE)and mean absolute percentage error(MAPE)were selected to evaluate the prediction results.Meanwhile,the prediction results of extreme gradient boosting(XGBoost)network,support vector regression(SVR)network and LSTM network were compared.The results demonstrate that the MIC-Res-LSTM model can improve the accuracy of the short-time significant wave height prediction values.
wave height predictionmaximum information coefficientresidual networklong and short-term memory networksupport vector regression
NETL
NSTL
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