PDF(Pubmed)
Abstract：
The profound impacts of severe air pollution on human health, ecological balance, and economic stability are undeniable. Precise air quality forecasting stands as a crucial necessity, enabling governmental bodies and vulnerable communities to proactively take essential measures to reduce exposure to detrimental pollutants. Previous research has primarily focused on predicting air quality using only time-series data. However, the importance of remote-sensing image data has received limited attention. This paper proposes a new multi-modal deep-learning model, Res-GCN, which integrates high spatial resolution remote-sensing images and time-series air quality data from multiple stations to forecast future air quality. Res-GCN employs two deep-learning networks, one utilizing the residual network to extract hidden visual information from remote-sensing images, and another using a dynamic spatio-temporal graph convolution network to capture spatio-temporal information from time-series data. By extracting features from two different modalities, improved predictive performance can be achieved. To demonstrate the effectiveness of the proposed model, experiments were conducted on two real-world datasets. The results show that the Res-GCN model effectively extracts multi-modal features, significantly enhancing the accuracy of multi-step predictions. Compared to the best-performing baseline model, the multi-step prediction\'s mean absolute error, root mean square error, and mean absolute percentage error increased by approximately 6%, 7%, and 7%, respectively.
摘要：
严重的空气污染对人类健康的深远影响，生态平衡,经济稳定是不可否认的。精确的空气质量预报是至关重要的，使政府机构和脆弱社区能够积极采取必要措施，减少有害污染物的暴露。以前的研究主要集中在仅使用时间序列数据来预测空气质量。然而,遥感图像数据的重要性受到的关注有限。本文提出了一种新的多模态深度学习模型,Res-GCN,它集成了来自多个站点的高空间分辨率遥感图像和时间序列空气质量数据，以预测未来的空气质量。Res-GCN采用两个深度学习网络，一种利用残差网络从遥感图像中提取隐藏的视觉信息，另一种使用动态时空图卷积网络从时间序列数据中捕获时空信息。通过从两种不同的模态中提取特征，可以实现改进的预测性能。为了证明该模型的有效性,实验是在两个真实世界的数据集上进行的。结果表明,Res-GCN模型有效地提取了多模态特征,显著提高了多步预测的准确性。与性能最佳的基线模型相比，多步预测的平均绝对误差，均方根误差，平均绝对百分比误差增加了大约6%，7%,7%,分别。
