海洋预报2024，Vol.41Issue(3)：22-32,11.DOI:10.11737/j.issn.1003-0239.2024.03.003
基于自注意力机制的深度学习的海洋三维温度场预测
Three-Dimensional(3-D)ocean temperature field prediction based on deep learning of self-attention mechanism
摘要
Abstract
While previous researches on the 3-D ocean temperature field prediction mainly focused on the perspective of spatial and temporal relationship which ignored the relationship of relative location,this article proposes a SA-ConvLSTM 3-D ocean temperature field prediction model which combines self-attention memory module and ConvLSTM.The new model is not only able to extract the spatial-temporal features in historical 3-D ocean temperature fields,but can obtain and record the information of location to learn the laws of seawater in both space and time.The experimental results show that the RMSE and MAE of the SA-ConvLSTM forecasts have approximately increased by 14%in sliding prediction and multi-step recursive prediction,and its overall performance is better than the persistence,LSTM and ConvLSTM model.Our research provides a new idea for the prediction of the 3-D seawater temperature field.关键词
海水温度/三维温度场预测/自注意力记忆机制/SA-ConvLSTM/多步长递归预测Key words
seawater temperature/3-D ocean temperature field prediction/self-attention memory/SA-Conv-LSTM/multi-step recursive prediction分类
海洋科学引用本文复制引用
岳伟豪,徐永生,朱善良..基于自注意力机制的深度学习的海洋三维温度场预测[J].海洋预报,2024,41(3):22-32,11.基金项目
崂山实验室科技创新项目(LSKJ202201406) （LSKJ202201406）
国家自然科学基金(41906027) （41906027）
国家自然科学基金联合基金项目(U22A20587) （U22A20587）
国家自然科学基金-山东联合基金重点项目(U1406401) （U1406401）
中国科学院战略先导计划(XDB42000000). （XDB42000000）
