Multistep-Ahead Prediction of Ocean SSTA Based on Hybrid Empirical Mode Decomposition and Gated Recurrent Unit Model

Abstract

The prediction of sea surface temperature anomalies (SSTA) is vital to the study of marine ecosystems and global climate. The SSTA can be accurately forecasted one step ahead by numerical and statistical methods. However, multistep-ahead forecasting for SSTA is greatly challenging since the nonlinearity and nonstationarity of SSTA and the lag problem of prediction. Therefore, in this article, a multistep-ahead SSTA forecasting method based on the hybrid empirical mode decomposition (EMD) and gated recurrent unit (GRU) model is proposed considering that EMD has the advantage of reducing data complexity and GRU is good at long-term prediction of data. First, the EMD algorithm is applied to obtain several intrinsic modal mode functions that are more stationary than the original data. Then, GRU is used for multistep forecasting, in which three multistep forecasting strategies (recursive, direct, and multioutput) are compared. The proposed hybrid model is validated by multistep forecasting for monthly average SSTA at the Niño3.4 region. The experimental results show that using reconstruction error as part of the prediction effectively improves the prediction accuracy of the EEMD-GRU model and outperforms other EMD algorithms combined with GRU. Compared with traditional models, the EEMD-GRU model can better predict future multimonth trends of SSTA and effectively alleviate the problem of prediction lag of the traditional model. Finally, this model is applied to the Niño3.4 regional SSTA prediction, and the results show that this model can provide a reference for ocean research.

Keywords

• Empirical mode decomposition
• gated recurrent unit neural network
• multistep-ahead prediction
• prediction lags
• sea surface temperature anomalies