PREDICTION OF SIGNIFICANT WAVE HEIGHT BASED ON MULTIVARIABLE DSD-LSTM MODEL
A new signal decomposition algorithm(DSD)is designed by using the ICEEMDAN and recursive quantification analysis method,which divides the original signal into deterministic and stochastic components.Considering the influence of wind speed and wind direction on wave height,a multi-variable DSD-LSTM model was established by combining DSD algorithm with Long and Short-Term Memory network(LSTM)to predict significant wave height.The proposed model significantly improved the prediction accuracy compared to the single LSTM model and has better prediction performance compared to the univariate hybrid model DSD-LSTM-u.
wave energywave height predictimtime seriessignal processingdeep learninglong and short-memory network
NETL
NSTL
万方数据
