集成SSA、LSTM和ARMA方法的海面高变化预测OA

With the increasing influence of global climate change on sea levels,accurate prediction of sea-surface-height variations is essential for marine environmental monitoring,disaster early warning,and coastal resource management.Using sea-surface-height observations from 28 tide gauge stations along the coast of Japan from 2004 to 2023,this study proposes a prediction framework that integrates Singular Spectrum Analysis(SSA),a Long Short-Term Memory(LSTM)model,and an Autoregressive Moving Average(ARMA)model.The proposed method is applied to predict sea surface height along the Japanese coast and is compared with an SSA+ARMA hybrid approach.The results indicate that the SSA+LSTM+ARMA model outperforms the conventional SSA+ARMA approach in predicting sea surface height for 2019 to 2023.Specifically,the annual Mean Absolute Error(MAE)is reduced by approximately 6％～8％relative to SSA+ARMA.Projections for 2024 to 2025 suggest that sea-surface-height variations will continue to exhibit historical periodicity without significant anomalies,with an overall stable trend.These findings demonstrate that the SSA+LSTM+ARMA model more effectively captures nonlinear dynamics and long-term trends in sea surface height,providing a useful reference for marine management and disaster early warning.