In this paper,a residual network imputation model based on graph convolution network(GCN)and self-attention mechanism(RGCN-SA)is proposed to solve the observational data missing problem.The model is constructed on self-attention mechanism and graph convolution.The self-attention mechanism is used to extract the time-dependent features of observational data,and the space-dependent features of buoys at different positions are obtained through graph convolution.Combined with the self-supervised training method,the model is trained and the final ocean data imputation model is obtained.Through comparative experiments,it is proved that the model can effectively obtain the temporal and spatial correlation features of buoy observations after training,and obtaina better imputation effect than other methods.The effectiveness of each module of the model is proved by the ablation experiment.
关键词
自注意力机制/图卷积网络/插补/浮标数据
Key words
self-attention mechanism/graph convolutional network/imputation/buoy data
维普
万方数据