Prediction Analysis of Sea Level Change in the China Adjacent Seas Based on Singular Spectrum Analysis and Long Short-Term Memory Network

Abstract

Considering the nonlinear and non-stationary characteristics of sea-level-change time series, this study focuses on enhancing the predictive accuracy of sea level change. The adjacent seas of China are selected as the research area, and the study integrates singular spectrum analysis (SSA) with long short-term memory (LSTM) neural networks to establish an SSA-LSTM hybrid model for predicting sea level change based on sea level anomaly datasets from 1993 to 2021. Comparative analyses are conducted between the SSA-LSTM hybrid model and singular LSTM neural network model, as well as (empirical mode decomposition) EMD-LSTM and (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) CEEMDAN-LSTM hybrid models. Evaluation metrics, including the root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R2), are employed for the accuracy assessment. The results demonstrate a significant improvement in prediction accuracy using the SSA-LSTM hybrid model, with an RMSE of 5.26 mm, MAE of 4.27 mm, and R2 of 0.98, all surpassing those of the other models. Therefore, it is reasonable to conclude that the SSA-LSTM hybrid model can more accurately predict sea level change.