An Improved VMD–EEMD–LSTM Time Series Hybrid Prediction Model for Sea Surface Height Derived from Satellite Altimetry Data

Abstract

Changes in sea level exhibit nonlinearity, nonstationarity, and multivariable characteristics, making traditional time series forecasting methods less effective in producing satisfactory results. To enhance the accuracy of sea level change predictions, this study introduced an improved variational mode decomposition and ensemble empirical mode decomposition–long short-term memory hybrid model (VMD–EEMD–LSTM). This model decomposes satellite altimetry data from near the Dutch coast using VMD, resulting in components of the intrinsic mode functions (IMFs) with various frequencies, along with a residual sequence. EEMD further dissects the residual sequence obtained from VMD into second-order components. These IMFs decomposed by VMD and EEMD are utilized as features in the LSTM model for making predictions, culminating in the final forecasted results. The experimental results, obtained through a comparative analysis of six sets of Dutch coastal sea surface height data, confirm the excellent accuracy of the hybrid model proposed (root mean square error (RMSE) = 47.2 mm, mean absolute error (MAE) = 33.3 mm, coefficient of determination (R2) = 0.9). Compared to the VMD-LSTM model, the average decrease in RMSE was 58.7%, the average reduction in MAE was 60.0%, and the average increase in R2 was 49.9%. In comparison to the EEMD-LSTM model, the average decrease in RMSE was 27.0%, the average decrease in MAE was 28.0%, and the average increase in R2 was 6.5%. The VMD–EEMD–LSTM model exhibited significantly improved predictive performance. The model proposed in this study demonstrates a notable enhancement in global mean sea lever (GMSL) forecasting accuracy during testing along the Dutch coast.

Keywords

• sea level change
• deep learning
• time series prediction
• VMD
• EEMD
• LSTM