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With a gradually rising global average sea level, it is of great significance to predict changes in the sea level. However, sea level variations often exhibit both linear and nonlinear characteristics, complicating the prediction of sea level changes with a single model. The seasonal autoregressive integrated moving average (SARIMA) model can fully consider the linear characteristics of time series, but its nonlinear prediction ability is poor; the long short-term memory (LSTM) model can compensate for this shortcoming. To predict complex sea level changes, we propose a strategy to combine the SARIMA and LSTM models to increase the sea level prediction accuracy. In our method, sea level anomaly (SLA) time series are decomposed into the trend and seasonal term and random term; then, the SARIMA model is used to predict the trend and seasonal term of sea level variations, whereas the random term is predicted by LSTM. Sea surface height data from 1993 to 2018 are used in an experiment. Compared with other models, the performance of the SARIMA+LSTM model is superior in predicting sea level changes with a minimum root mean square error of 1.155 cm and a maximum determination coefficient (R<sup>2</sup>) of 0.89 during the testing period. Furthermore, the predicted results are in close agreement with the SLA data, which indicates that the SARIMA+LSTM model could be successfully used for the estimation of sea level variability.

Estimation of Sea Level Variability in the China Sea and Its Vicinity Using the SARIMA and LSTM Models