Regional Sea Level Change Prediction with Integration of Singular Spectrum Analysis and Long-short-term Memory Network

Abstract:

In this paper， the China’s first global ocean climate data records （CDRs） are used to analyze and predict the sea level changes in the Yellow Sea with obvious seasonal changes. Based on the singular spectrum analysis （SSA）， the time and spatio-temporal series of sea level anomalies （SLAs） in the Yellow Sea are decomposed and de-noised. Then the SSA-long short-term memory （LSTM） network （SSA-LSTM combined model） is established to predict the sea level trends of the Yellow Sea. Compared with the traditional methods， the prediction accuracy of the SSA-LSTM combined model is significantly improved with 35.04 mm of the minimum root-mean-square error for the SLAs time series prediction length of 5 years. For the first-year prediction of spatial-temporal series of SLAs， the minimum root-mean-square error is only 19.68 mm. The law of spatial-temporal differentiation of the sea level change in the Yellow Sea is also analyzed by the spatial modes. It is found that the sea level trend of the Yellow Sea is highly consistent and significantly related to the season and latitude. According to the SSA-LSTM combined model， the sea level rise rate of the Yellow Sea will remain at 3.65±0.79 mm per year from 2016 to 2025.

Key words:sea level anomaly (SLA);spatio-temporal series;singular spectrum analysis (SSA);long short-term memory (LSTM) network;the Yellow Sea