Liu Hexiang, Han Qilong, Sun Hui, Sheng Jingyu, Yang Ziyu
College of Computer Science and Technology, Harbin Engineering University, Harbin, China.
Institute of Systems Engineering, Academy of Military Sciences, Beijing, 100089, China.
Sci Rep. 2023 Aug 16;13(1):13335. doi: 10.1038/s41598-023-39286-0.
Air pollution is a leading cause of human diseases. Accurate air quality predictions are critical to human health. However, it is difficult to extract spatiotemporal features among complex spatiotemporal dependencies effectively. Most existing methods focus on constructing multiple spatial dependencies and ignore the systematic analysis of spatial dependencies. We found that besides spatial proximity stations, functional similarity stations, and temporal pattern similarity stations, the shared spatial dependencies also exist in the complete spatial dependencies. In this paper, we propose a novel deep learning model, the spatiotemporal adaptive attention graph convolution model, for city-level air quality prediction, in which the prediction of future short-term series of PM2.5 readings is preferred. Specifically, we encode multiple spatiotemporal dependencies and construct complete spatiotemporal interactions between stations using station-level attention. Among them, we design a Bi-level sharing strategy to extract shared inter-station relationship features between certain stations efficiently. Then we extract multiple spatiotemporal features with multiple decoders, which it is extracted from the complete spatial dependencies between stations. Finally, we fuse multiple spatiotemporal features with a gating mechanism for multi-step predictions. Our model achieves state-of-the-art experimental results in several real-world datasets.
空气污染是人类疾病的主要成因。准确的空气质量预测对人类健康至关重要。然而，在复杂的时空依赖关系中有效提取时空特征并非易事。大多数现有方法侧重于构建多个空间依赖关系，而忽略了对空间依赖关系的系统分析。我们发现，除了空间邻近站点、功能相似站点和时间模式相似站点外，完整的空间依赖关系中还存在共享空间依赖关系。在本文中，我们提出了一种新颖的深度学习模型——时空自适应注意力图卷积模型，用于城市级空气质量预测，其中更侧重于对未来短期PM2.5读数序列进行预测。具体而言，我们对多个时空依赖关系进行编码，并使用站点级注意力构建站点之间完整的时空交互。其中，我们设计了一种双层共享策略，以高效提取特定站点之间的共享站点间关系特征。然后，我们使用多个解码器提取多个时空特征，这些特征是从站点之间完整的空间依赖关系中提取的。最后，我们通过门控机制融合多个时空特征以进行多步预测。我们的模型在多个真实世界数据集上取得了领先的实验结果。
