基于自适应层级图卷积的多站点空气质量预测模型

时空预测任务在污染治理、交通、能源、气象等领域应用广泛. PM_2.5浓度预测作为典型的时空预测任务, 需要对空气质量数据中的时空依赖关系进行分析和利用. 现有时空图神经网络(ST-GNNs)研究所使用的邻接矩阵使用启发式规则预定义, 无法准确表示站点之间的真实关系. 本文提出了一种自适应分层图卷积神经网络(AHGCNN)用于PM_2.5预测. 首先, 引入了一种分层映射图卷积架构, 在不同层级上使用不同的自学习邻接矩阵, 以有效挖掘不同站点之间独特的时空依赖. 其次, 以基于注意力的聚合机制连接上下层邻接矩阵, 加速收敛过程. 最后, 将隐藏的空间状态与门控循环单元相结合, 形成一个统一的预测架构, 同时捕捉多层次的空间依赖关系和时间依赖关系, 提供最终的预测结果. 实验中, 我们与7种主流预测模型进行对比, 结果表明该模型可以有效获取空气监测站点之间的时空依赖, 提高预测精确度.

Multi-site Air Quality Forecasting Model Using Adaptive Hierarchical Graph Convolution

Spatiotemporal forecasting finds extensive applications in domains such as pollution management, transportation, energy, and meteorology. Predicting PM_2.5 concentration, as a quintessential spatiotemporal forecasting task, necessitates the analysis and utilization of spatiotemporal dependencies within air quality data. Existing studies on spatiotemporal graph neural networks (ST-GNNs) either employ predefined heuristic rules or trainable parameters for adjacency matrices, posing challenges in accurately representing authentic inter-station relationships. This study introduces the adaptive hierarchical graph convolutional neural network (AHGCNN) to address these issues concerning PM_2.5 prediction. Firstly, a hierarchical mapping graph convolutional architecture is introduced, employing distinct self-learning adjacency matrices at different hierarchical levels, efficiently uncovering unique spatiotemporal dependencies among various monitoring stations. Secondly, an attention-based aggregation mechanism is employed to connect adjacency matrices across different hierarchical levels, expediting the convergence process. Finally, the hidden spatial states are fused with gated recurrent unit (GRU), forming a unified predictive framework capable of concurrently capturing multi-level spatial and temporal dependencies, ultimately delivering the prediction results. In the experiments, the proposed model is comparatively analyzed with seven mainstream models. The results indicate that the model can effectively capture the spatiotemporal dependencies between air monitoring stations, improving predictive accuracy.