基于自注意力机制的深度学习的海洋三维温度场预测OA北大核心CSTPCD

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.