Sea level change is characterized by non-stationarity,nonlinearity and multi-time scale,it is difficult to predict the future sea level change accurately.The paper proposes a combined prediction model integrating SSA and LSTM,a short-term forecast analysis of global sea level change is carried out using the sea level anomalies(SLA)data from 1993 to 2020 provided by AVISO.Firstly,SSA decomposition was used to extract sub-sequences such as long-term trends,periodic terms and the residual of the SLA data to reduce the complexity of the original sequence.Then,LSTM models were constructed for each sub-sequence to predict the future changes.Finally the predicted values of all the sub-sequences were reconstructed to obtain the final prediction result of SLA.Compared with the direct prediction of LSTM and SSA-ARIMA combined model,the SSA-LSTM combined model has better prediction effect.Based on the SSA-LSTM combined model,the global sea level rise rate in 2021-2025 is about 3.96 mm/a.
关键词
海平面高度异常/奇异谱分析/长短期记忆网络/时间序列/短期预测
Key words
sea level anomaly/singular spectrum analysis/short and long term memory network/time series/short-term forecast
维普
万方数据