海洋测绘2024，Vol.44Issue(3)：53-57,61,6.DOI:10.3969/j.issn.1671-3044.2024.03.011
融合IVMD的海表温度时空智能预测方法
SST spatio-temporal intelligent prediction method integrating IVMD
摘要
Abstract
Accurate Sea Surface Temperature(SST)prediction is vital in marine and meteorological fields,such as marine fisheries and marine weather forecasting.A spatio-temporal hybrid model based on Improved Variational Mode Decomposition(IVMD)is proposed to predict SST.The Variational Mode Decomposition(VMD)method was improved by central frequency observation,residual index minimization and Pearson correlation coefficient to remove SST sequence redundancy.The Graph Convolutional Network(GCN)was adopted to extract SST interaction features,and Long Short-Term Memory(LSTM)was introduction to capture time dynamics.Combination of the above two model can enhance prediction accuracy.The East China Sea was selected for empirical analysis.Experimental results show that,compared with the existing model,the proposed model has significantly improved the root mean square error,mean absolute error and mean absolute percentage error.The effectiveness and stability of the proposed model are verified.关键词
海洋表面温度预测/改进变分模态分解/皮尔逊相关系数/图卷积神经网络/长短时记忆网络Key words
prediction of sea surface temperature/improved variational mode decomposition/pearson correlation coefficients/graph convolutional network/long short-term memory network分类
天文与地球科学引用本文复制引用
韩莹,曹允重,张凌珺,赵芮晗,董昌明..融合IVMD的海表温度时空智能预测方法[J].海洋测绘,2024,44(3):53-57,61,6.基金项目
国家自然科学基金(62076136) （62076136）
南方海洋科学与工程广东省实验室(珠海)基金(SML2020SP007). （珠海）
