Accurately forecasting air quality is critical to protecting general public from lung and heart diseases. This is a challenging task due to the complicated interactions among distinct pollution sources and various other influencing factors. Existing air quality forecasting methods cannot effectively model the diffusion processes of air pollutants between cities and monitoring stations, which may suddenly deteriorate the air quality of a region. In this paper, we propose HighAir, i.e., a hierarchical graph neural network-based air quality forecasting method, which adopts an encoder-decoder architecture and considers complex air quality influencing factors, e.g., weather and land usage. Specifically, we construct a city-level graph and station-level graphs from a hierarchical perspective, which can consider city-level and station-level patterns, respectively. We design two strategies, i.e., upper delivery and lower updating, to implement the inter-level interactions, and introduce message passing mechanism to implement the intra-level interactions. We dynamically adjust edge weights based on wind direction to model the correlations between dynamic factors and air quality. We compare HighAir with the state-of-the-art air quality forecasting methods on the dataset of Yangtze River Delta city group, which covers 10 major cities within 61,500 km2. The experimental results show that HighAir significantly outperforms other methods.
翻译:准确预测空气质量对于保护公众免受肺和心脏疾病影响至关重要。由于不同污染源和各种其他影响因素之间的复杂互动,这是一项具有挑战性的任务。现有的空气质量预测方法无法有效地模拟城市和监测站之间的空气污染物扩散过程,这可能会突然使一个区域的空气质量恶化。在本文中,我们提议高空,即一个基于气质网络的高层图形神经质量预测方法,该方法采用一种编码器分解器分解器结构,并考虑复杂的空气质量影响因素,例如天气和土地使用。具体地说,我们从等级角度构建一个城市一级的图表和站级图表,其中可以分别考虑城市一级和站级的格局。我们设计了两种战略,即上层交付和低更新,以实施层次之间的相互作用,并引入信息传递机制,以实施一级内部互动。我们根据风向动态因素和空气质量的边缘重量进行动态调整,以模拟动态因素和空气质量之间的相互关系。我们将高空机与州一级的空气质量预测方法进行比较,我们从等级角度分别考虑城市一级和站一级图表,其中可以分别考虑城市一级和站级模式的模式。我们设计出城市一级和站级的格局模式模式。我们设计了两种战略,即高层交付和低端更新,用以展示长方数据,以显示高位轨道内的主要三角城市的数据。