关键词: 空气质量预测, 多域特征融合, 时空特征, 周期解耦, 门控机制融合, 图神经网络

Abstract:

In the spatiotemporal hybrid models that integrate meteorological， spatial， and temporal information， the modeling of temporal changes is usually done in one-dimensional space. To solve the problems that one-dimensional sequences are limited in sliding windows and is lack of the flexibility of multi-scale feature extraction， a Multi-domain SpatioTemporal Hierarchical Graph Neural Network （MST-HGNN） model was proposed. Firstly， two levels of hierarchical graphs were constructed， namely， city-wide global scale one and station-level local scale one， so as to perform spatial relationship learning. Secondly， the one-dimensional air quality sequences were transformed into a set of two-dimensional tensors based on multiple periods， and multi-scale convolution in two-dimensional space was used to capture frequency domain features by periodic decoupling. At the same time， Long Short-Term Memory （LSTM） network in one-dimensional space was employed to fit temporal features. Finally， to avoid redundant information aggregation， a gating mechanism fusion module was designed for multi-domain feature fusion of frequency domain and temporal domain features. Experimental results on Urban-Air dataset and the Yangtze River Delta city cluster dataset show that compared with Multi-View Multi-Task Spatiotemporal Graph Convolutional Network model （M2）， the proposed model has lower Mean Absolute Error （MAE） and Root Mean Squared Error （RMSE） than the comparison model in predicting air quality at the 1 h， 3 h， 6 h， and 12 h. It can be seen that MST-HGNN can decouple complex time patterns in the frequency domain， compensate for the limitations of temporal feature modeling using frequency domain information， and predict air quality changes more comprehensively by combining time domain information.

Key words: air quality prediction, multi-domain feature fusion, spatiotemporal feature, periodic decoupling, gating mechanism fusion, Graph Neural Network (GNN)