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Rising sea level is of great significance to coastal societies; predicting sea level extent in coastal regions is critical. When carrying out predictions, the subsequences obtained using decomposition methods may exhibit a certain regularity and therefore can provide multidimensional information that can be used to improve prediction models. Traditional decomposition methods such as seasonal and trend decomposition using Loess (STL) focus mostly on the fluctuating trend of time series and ignore its impact on prediction. Methods in the signal decomposition domain, such as variational mode decomposition (VMD), have no physical significance. In response to the above problems, a new decomposition method for sea level anomaly time series prediction (DMSLAP) is proposed. With this method, the trend term in a time series can be isolated and the effects of abnormal sea level change behaviors can be attenuated. We decompose multiperiod characteristics using this method while maintaining the smoothness of the analyzed series. Satellite altimetry data from 1993 to 2020 are used in experiments conducted in the study area. The results are then compared with predictions obtained using existing decomposition methods such as the STL and VMD methods and time varying filtering based on empirical mode decomposition (TVF-EMD). The performance of DMSLAP combined with a prediction method resulted in optimal sea level anomaly (SLA) predictions, with a minimum root mean square error (RMSE) of 1.40 cm and a maximum determination coefficient (R2) of 0.93 during 2020. The DMSLAP method was more accurate when predicting 1-year data and 3-year data. The TVF-EMD and DMSLAP methods had comparable accuracies, and the periodic term decomposed by the DMSLAP method was more in line with the actual law than that derived using the TVF-EMD method. Thus, DMSLAP can decompose SLA time series better than existing methods and is an effective tool for obtaining short-term SLA prediction. [ABSTRACT FROM AUTHOR]

The South China Sea is China's largest marginal sea area, and it is rich in oil and gas mineral resources; thus, estimating its sea level changes is of practical significance. Based on linear and nonlinear sea level change characteristics, this paper decomposes 1992–2019 monthly mean sea level anomaly time series in the South China Sea into trend, seasonal, and random terms. This paper compares the seasonal autoregressive integrated moving average (SARIMA) and Prophet models for estimating the trend and seasonal terms and the long short-term memory (LSTM) and radial basis function (RBF) models for estimating random terms, and the more suitable models were selected. A Prophet-LSTM combined model was developed based on the accuracy results. This paper uses the combined model to study the effect of known data length on the experimental results and determines the best prediction duration. The results show that the combined model is suitable for short-term and medium-term estimations of 12–36 months. The accuracy at 36 months is 0.962 cm, which proves that the combined model has high application value for estimating sea level changes in the South China Sea. [ABSTRACT FROM AUTHOR]