海洋科学2025，Vol.49Issue(5)：1-12,12.DOI:10.11759/hykx20231127001
基于VMD-SSA-LSTM的海面温度预报
Sea surface temperature forecasting based on VMD-SSA-LSTM
摘要
Abstract
Sea surface temperature(SST)is a key parameter in the ocean-atmosphere interaction,which is crucial for tropical cyclone track and intensity prediction and global climate change research.However,the traditional long short-term memory(LSTM)neural networks provide SST forecasts with low accuracy under extreme low and high tem-peratures.In order to improve the performance of SST forecasting,this paper proposes a combined variational modal decomposition(VMD)-sparrow search algorithm(SSA)-LSTM model.This model first uses VMD to decompose the nonsmooth and nonlinear SST time series into multiple eigenmodal functions with central frequencies.Subsequently,it adopts SSA to optimize the structure and parameters of LSTM to improve the model's learning ability and generalization ability.The model was experimentally validated using SST data of the South China Sea for 82 years taken from ERA5 reanalysis.The results showed that compared with the traditional LSTM model,the VMD-SSA-LSTM model considera-bly improves the forecast accuracy under extreme temperature conditions:the average root mean squared error of the proposed model is lower by 67%,the mean absolute percentage error is lower by 65.8%,and the mean absolute error is lower by 65.6%.This result demonstrated the great advantage of the combined model in dealing with complex nonlinear climate variables.This study provides a new approach for building high-precision intelligent forecasting models for SST and provides theoretical support for monitoring and early warning of extreme climate events.关键词
海面温度预报/长短时记忆/变模态分解/麻雀搜索算法Key words
sea surface temperature forecasting/long short-term memory/variational mode decomposition/sparrow search algorithm分类
海洋科学引用本文复制引用
李泽荣,林良师,张树刚,刘秀杰,叶佳承,王和锋,于华明,真世昕..基于VMD-SSA-LSTM的海面温度预报[J].海洋科学,2025,49(5):1-12,12.基金项目
洞头区科技研发项目(S2023Y09) （S2023Y09）
国家社科基金(23CGL008) （23CGL008）
国家重点研发计划项目(2022YFD2401304)[Dongtou District Science and Technology R&D Project,No.S2023Y09 （2022YFD2401304）
National Social Science Foundation of China,No.23CGL008 （）
National Key Research and Development Program of China,No.2022YFD2401304] （）
