A GRU-sea surface temperature prediction model integrating graph convolution and an attention mechanism
Sea surface temperature(SST)is a key factor that affects oceanic climate changes;consequently,the accurate prediction of SST is of great significance in related fields such as oceanic meteorology and navigation.To simultaneously capture spatiotemporal correlations between the SST data,this paper proposes a gated recurrent unit(GRU)-SST prediction model(graph convolutional recurrent unit-attention mechanism,GCRU-ATT)that com-bines graph convolution(GC)and an attention mechanism.A sea surface space is modeled into a graphical topo-logical structure through GC,which is subsequently used to effectively mine the unique spatial features of the SST data.Initially,in the GRU,matrix multiplication is replaced with GC and GCRU layers are formed.These GCRU layers are then used to build the main structure of the model to extract the spatiotemporal information of the data.Further,an attention mechanism is introduced to assign different weights to the output information of the GCRU layers.Finally,a fully connected output layer is used to output the SST prediction results.To select SST data from the East China Sea and Bohai Sea for modeling,experimental results show that the GCRU-ATT model exhibits superior robustness,smaller error index values,and higher prediction accuracy than existing methods.
万方数据
维普
