Spatiotemporal feature-based GCN-LSTM model for predicting sand-dust weather in northwest China
Aiming at the issue of insufficient spatiotemporal feature extraction in previous sand and dust weather prediction algorithms,this paper proposes a spatiotemporal feature prediction model based on graph convolution and long short-term memory network(GCN-LSTM)fused with spatiotemporal features.Taking Northwest China as the research object,the vegetation index and distance between cities are used to construct an adjacency matrix,spatial features are extracted by graph convolutional network(GCN),and long short-term memory net-work(LSTM)are used to extract temporal features,and the features are fused to predict the dust weather of each city.Compared with the GCN,LSTM,and spatiotemporal causal convolutional neural network(STCN)models,the accuracy of the GCN-LSTM model proposed in this paper is improved by 6％,8％and 2％,respectively,and its receiver operation characteristic curve(ROC),area under ROC curve(AUC),and accuracy-recall curve(P-R)evaluation indicators are better.It provides some references for taking preventive measures and reducing losses in sand and dust weather.
NETL
NSTL
万方数据
