山东科学2026，Vol.39Issue(4)：12-19,8.DOI:10.3976/j.issn.1002-4026.2025084
集成SSA、LSTM和ARMA方法的海面高变化预测
Integrated SSA-LSTM-ARMA framework for sea-surface-height prediction
摘要
Abstract
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.关键词
海面高/奇异谱分析/长短期记忆神经网络/自回归移动平均/验潮站/日本海/预测方法Key words
sea surface height/Singular Spectrum Analysis/Long Short-Term Memory neural network/Autoregressive Moving Average/tide gauge/coast of Japan/prediction method分类
海洋科学引用本文复制引用
张润达,王建全,张宁..集成SSA、LSTM和ARMA方法的海面高变化预测[J].山东科学,2026,39(4):12-19,8.基金项目
中国科学院海岸带环境过程与生态修复重点实验室开放基金(2018KFJJ04) （2018KFJJ04）
