用时空模型填补空气质量监测空白，提升城市污染预测精度
Deep Spatio-Temporal Neural Network for Air Quality Reanalysis
- 结合LSTM与注意力机制建模时间趋势，引入循环编码保持时间连续性
- 通过神经kNN实现基于特征的插值，精准补全未观测站点数据
- 在华北2013-2017年PM2.5数据上表现优异，适合城市环境动态建模
空气质量预测对减轻健康影响和指导决策至关重要，但现有模型多关注时间趋势而忽视空间泛化。本文提出AQ-Net，一种用于近未来已知与未知监测站的时空重分析模型。该模型采用LSTM与多头注意力进行时间回归，并提出循环编码技术以确保时间表示的连续性。为实现精细的空间空气质量估计，将AQ-Net与神经kNN结合，利用特征基插值填补粗粒度观测站之间的空间空白。基于2013-2017年华北地区数据进行的大量实验表明，AQ-Net在空气质量重分析任务中表现优异，凸显混合时空模型在捕捉环境动态中的潜力，尤其适用于空间与时间变化均显著的城市区域。
原文摘要 · Abstract (English)
Air quality prediction is key to mitigating health impacts and guiding decisions, yet existing models tend to focus on temporal trends while overlooking spatial generalization. We propose AQ-Net, a spatiotemporal reanalysis model for both observed and unobserved stations in the near future. AQ-Net utilizes the LSTM and multi-head attention for the temporal regression. We also propose a cyclic encoding technique to ensure continuous time representation. To learn fine-grained spatial air quality estimation, we incorporate AQ-Net with the neural kNN to explore feature-based interpolation, such that we can fill the spatial gaps given coarse observation stations. To demonstrate the efficiency of our model for spatiotemporal reanalysis, we use data from 2013-2017 collected in northern China for PM2.5 analysis. Extensive experiments show that AQ-Net excels in air quality reanalysis, highlighting the potential of hybrid spatio-temporal models to better capture environmental dynamics, especially in urban areas where both spatial and temporal variability are critical.
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