A hybrid EMD-LSTM model for non-stationary wave prediction in offshore China

Abstract

Accurate wave forecasts provide vital safety guarantees for marine operations of ships. The difficulty in the study of ocean wave forecasting is that ocean waves have non-linear and non-stationary characteristics. The Auto-Regressive (AR) model is widely used in time series forecasting, but it is weak in processing non-linearity and non-stationarity. The Long Short Term Memory (LSTM) model developed for timing problems has strong non-linear processing capabilities, but there are shortcomings in the treatment of non-stationarity. Empirical Mode Decomposition (EMD) can effectively separate non-linearity and non-stationarity in data. This paper combines the advantages of the LSTM model and EMD and proposes an EMD-LSTM method. Based on significant wave heights from three locations offshore China, it has been found that the error of the EMD-LSTM method is lower than that of the LSTM model. Meanwhile, it has been found that the EMD-LSTM method can increase the forecast time in advance by more than doubled under the same tolerance by analyzing the forecast effect of different forecast times. It has been proved that the EMD-LSTM model has good superiority for the prediction of non-linear and non-stationary waves.