Search

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]