Abstract: Because the air quality in different regions of the city are correlated with each other in both time and space, the traditional deep learning model structure is relatively simple, and it is difficult to model from the perspectives of time and space. Aiming at this problem, a Spatio Temporal Air Quality Index (STAQI) model that can simultaneously extract the complex spatial and temporal relationships between air qualities was proposed for air quality prediction. The model was composed of local components and global components, which were used to describe the influences of local pollutant concentration and air quality states of adjacent sites on the air quality prediction of target site, and the prediction results were obtained by using the weighted fusion component output. In the global component, the graph convolutional network was used to improve the input part of the gated recurrent unit network, so as to extract the spatial characteristics of the input data. Finally, STAQI model was compared with various baseline models and variant models. Among them, the Root Mean Square Error (RMSE) of STAQI model is decreased by about 19% and 16% respectively compared with those of the gated recurrent unit model and the global component variant model. The results show that STAQI model has the best prediction performance for any time window, and the prediction results of different target sites verify the strong generalization ability of the model.

Key words: air quality prediction, spatio-temporal data, Graph Convolutional Network (GCN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), deep learning

摘要： 由于城市中各区域空气质量同时存在时间与空间维度上的相关性，而传统深度学习模型结构比较单一，并且难以从时空角度进行建模。针对该问题提出一种可以同时提取空气质量间复杂时空关系的STAQI模型用于空气质量预测。该模型由局部组件和全局组件构成，分别用于描述本地污染物浓度和邻近站点空气质量状况对目标站点空气质量预测产生的影响，并利用加权融合组件输出获得预测结果。在全局组件中，利用图卷积网络改进门控循环单元网络的输入部分，从而提取出输入数据中的空间特征。最后将STAQI模型与多种基准模型和变体模型进行对比。其中，STAQI模型与门控循环单元模型和全局组件变体模型相比，均方根误差（RMSE）分别下降约19%和16%。结果表明STAQI模型对于任意时间窗口都具有最佳预测性能，并且对不同目标站点的预测结果验证了该模型具有较强的泛化能力。

关键词: 空气质量预测, 时空数据, 图卷积网络, 长短期记忆, 门控循环单元, 深度学习