基于多尺度时空优化的空气质量预测方法OA

In this article we propose an air quality prediction method based on Multi-Scale Spatial-Temporal Network for air quality prediction(MSSTN-AQP)to improve the accuracy of long-term air quality prediction by combining the long-and short-term time dependence and dynamic spatial dependence in the air quality system.First,the spatial-temporal features are extracted from multi-source heterogeneous data by constructing a multi-scale spatial-temporal feature extraction module.Second,the dynamic spatial feature extraction module is constructed.With an effective combination of the graph convolu-tional network with the attention mechanism,the global spatial features in the air quality network are captured and used for the joint modeling of multiple spatial dependencies.Finally,the temporal feature extraction module is constructed,which is to improve and optimize the Transformer model.The adaptive time Transformer module is mainly used to simulate bidirec-tional time dependencies across multiple time steps.Moreover,the above temporal feature extraction module is effectively integrated to construct an end-to-end air quality prediction model.To verify the effectiveness of MSSTN-AQP,extensive experiments were conducted on two real data sets.The experimental results showed that MSSTN-AQP was more advanta-geous in prediction accuracy,especially in long-term air quality prediction tasks.