Fine particulate matter such as PM2.5 threatens significantly to the environment and human health, so it is essential to design a reliable long-term prediction method for PM2.5 concentrations. Existing long-term PM2.5 prediction models inadequately utilize urban spatial features, fail to consider the role of meteorological factors in PM2.5 levels, and overlook the interaction between PM2.5 concentrations in different cities. To tackle this issue, we propose two new models and integrate them. Firstly, we develop a spatial feature model (ECGAT) for extracting PM2.5 concentration among regions based on Graph Neural Networks (GNN), edge-channel mechanisms, and Graph Attention Convolution (GATConv). This model utilizes GNN to extract urban adjacency relationships and meteorological features, employs edge-channel mechanisms to recalculate weights for interactions between cities, and outputs spatial correlations through GATConv. Secondly, we propose Gating Closed-form Continuous-time Neural Networks (GCFC) as a temporal model to extract the PM2.5 concentration's temporal features. The fusion of these two models, named ECGAT-GCFC (EGCFC), enhances the model's capability to capture spatiotemporal features and improves performance in PM2.5 long-term predictions. Results from real-world data analysis show that the proposed algorithm outperforms state-of-the-art existing prediction models in predicting PM2.5 levels over long durations. Compared to baseline models, EGCFC reduces RMSE by an average of 3.39 %, decreases MAE by 4.83 %, increases R2 by 4.89 %, CSI by 3.13 %, and lowers FAR by 11.39 %. These indicate that EGCFC is an effective method for predicting trends in urban PM2.5 concentrations.

中文翻译：

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使用边缘通道图注意力网络和门闭式连续时间神经网络的 PM2.5 浓度长期预测方法


PM2.5等细颗粒物对环境和人类健康构成严重威胁，因此设计可靠的PM2.5浓度长期预测方法至关重要。现有的PM2.5长期预测模型未能充分利用城市空间特征，没有考虑气象因素对PM2.5水平的影响，也忽视了不同城市PM2.5浓度之间的相互作用。为了解决这个问题，我们提出了两种新模型并将它们集成。首先，我们开发了一个基于图神经网络（GNN）、边缘通道机制和图注意卷积（GATConv）的空间特征模型（ECGAT）来提取区域之间的 PM2.5 浓度。该模型利用GNN提取城市邻接关系和气象特征，利用边缘通道机制重新计算城市之间相互作用的权重，并通过GATConv输出空间相关性。其次，我们提出门控闭式连续时间神经网络（GCFC）作为时间模型来提取 PM2.5 浓度的时间特征。这两个模型的融合，称为 ECGAT-GCFC (EGCFC)，增强了模型捕获时空特征的能力，并提高了 PM2.5 长期预测的性能。现实世界数据分析的结果表明，所提出的算法在长期预测 PM2.5 水平方面优于最先进的现有预测模型。与基线模型相比，EGCFC 平均使 RMSE 降低 3.39%，MAE 降低 4.83%，R2 提高 4.89%，CSI 提高 3.13%，FAR 降低 11.39%。这些表明EGCFC是预测城市PM2.5浓度趋势的有效方法。