Chen Quanchao, Ding Ruyan, Mo Xinyue, Li Huan, Xie Linxuan, Yang Jiayu
School of Cyberspace Security/School of Cryptology, Hainan University, Haikou, China.
Sci Rep. 2024 Feb 22;14(1):4408. doi: 10.1038/s41598-024-55060-2.
In recent years, air pollution has become increasingly serious and poses a great threat to human health. Timely and accurate air quality prediction is crucial for air pollution early warning and control. Although data-driven air quality prediction methods are promising, there are still challenges in studying spatial-temporal correlations of air pollutants to design effective predictors. To address this issue, a novel model called adaptive adjacency matrix-based graph convolutional recurrent network (AAMGCRN) is proposed in this study. The model inputs Point of Interest (POI) data and meteorological data into a fully connected neural network to learn the weights of the adjacency matrix thereby constructing the self-ringing adjacency matrix and passes the pollutant data with this matrix as input to the Graph Convolutional Network (GCN) unit. Then, the GCN unit is embedded into LSTM units to learn spatio-temporal dependencies. Furthermore, temporal features are extracted using Long Short-Term Memory network (LSTM). Finally, the outputs of these two components are merged and air quality predictions are generated through a hidden layer. To evaluate the performance of the model, we conducted multi-step predictions for the hourly concentration of PM, PM and O at Fangshan, Tiantan and Dongsi monitoring stations in Beijing. The experimental results show that our method achieves better predicted effects compared with other baseline models based on deep learning. In general, we designed a novel air quality prediction method and effectively addressed the shortcomings of existing studies in learning the spatio-temporal correlations of air pollutants. This method can provide more accurate air quality predictions and is expected to provide support for public health protection and government environmental decision-making.
近年来，空气污染日益严重，对人类健康构成了巨大威胁。及时、准确的空气质量预测对于空气污染预警和控制至关重要。尽管数据驱动的空气质量预测方法前景广阔，但在研究空气污染物的时空相关性以设计有效的预测器方面仍存在挑战。为了解决这个问题，本研究提出了一种名为基于自适应邻接矩阵的图卷积循环网络（AAMGCRN）的新型模型。该模型将兴趣点（POI）数据和气象数据输入到全连接神经网络中，以学习邻接矩阵的权重，从而构建自环邻接矩阵，并将以该矩阵为输入的污染物数据传递给图卷积网络（GCN）单元。然后，将GCN单元嵌入到长短期记忆（LSTM）单元中，以学习时空依赖性。此外，使用长短期记忆网络（LSTM）提取时间特征。最后，将这两个组件的输出进行合并，并通过隐藏层生成空气质量预测。为了评估模型的性能，我们对北京房山、天坛和东四监测站的PM、PM和O的每小时浓度进行了多步预测。实验结果表明，与其他基于深度学习的基线模型相比，我们的方法取得了更好的预测效果。总体而言，我们设计了一种新颖的空气质量预测方法，有效地解决了现有研究在学习空气污染物时空相关性方面的不足。该方法可以提供更准确的空气质量预测，有望为公共卫生保护和政府环境决策提供支持。
