Application of a novel signal decomposition prediction model in minute sea level prediction

Although the prediction of hourly sea level and monthly mean sea level has been widely discussed, the prediction of minute sea level (MSL) has rarely been attempted under the background of human activities and climate change. The prediction of MSL is of great significance for real-time sea level measurements and tsunami early warnings. To improve the prediction accuracy of MSL, we propose several signal decomposition prediction models: the TVF-EMD-ENN model constructed using time varying filtering based empirical mode decomposition (TVF-EMD) and the Elman neural network (ENN); the WT-ENN model constructed using wavelet transform (WT) and ENN; and the CEEMD-ENN model constructed using complementary ensemble empirical mode decomposition (CEEMD) and ENN. These models first decompose the MSL into several subcomponents using different signal decomposition methods (i.e., TVF-EMD, WT, and CEEMD), and then the subcomponents are predicted by ENN. Lastly, the predicted values from the subcomponents are compiled to obtain the predicted MSL values. We applied these models to stations in six different countries, and the results showed that the TVF-EMD-ENN model was the most robust and had the best predictive performance. The prediction performances of WT-ENN and CEEMD-ENN models were slightly inferior to TVF-EMD-ENN. When the MSL sequence length was shortened, the TVF-EMD-ENN still performed best and achieved relatively robust prediction performance. The prediction performance of the ENN model was unstable with large differences among stations. Our study emphasizes the superiority of signal decomposition prediction models in solving MSL series predictions, and provides a new method and an important reference for solving non-stationary MSL prediction problems.

Automated summary

This study discusses the prediction of minute sea level and proposes several signal decomposition prediction models. The results show that the TVF-EMD-ENN model has the best predictive performance and can provide an important reference for measuring minute sea level and tsunami early warnings.