论文标题
星：使用多模式方法对空气质量的时空预测
STAR: Spatio-Temporal Prediction of Air Quality Using A Multimodal Approach
论文作者
论文摘要
随着全球经济活动的增加和高能需求，许多国家对空气污染引起了人们的关注。但是，由于许多因素的复杂相互作用，空气质量预测是一个具有挑战性的问题。在本文中，我们提出了一种用于时空空气质量预测的多模式方法。我们的模型了解了关键因素的多模式融合，以预测未来的空气质量水平。根据数据的分析，我们还评估了关键因素对空气质量预测的影响。我们对两个现实世界污染数据集进行了实验。对于首尔数据集，与基础线相比，我们的方法分别在PM2.5和PM10的长期预测中，平均绝对误差的平均绝对误差提高了11％和8.2％。与以前在中国1年数据集的最新结果相比，我们的方法还将PM2.5预测的平均绝对误差降低了20％。
With the increase of global economic activities and high energy demand, many countries have raised concerns about air pollution. However, air quality prediction is a challenging issue due to the complex interaction of many factors. In this paper, we propose a multimodal approach for spatio-temporal air quality prediction. Our model learns the multimodal fusion of critical factors to predict future air quality levels. Based on the analyses of data, we also assessed the impacts of critical factors on air quality prediction. We conducted experiments on two real-world air pollution datasets. For Seoul dataset, our method achieved 11% and 8.2% improvement of the mean absolute error in long-term predictions of PM2.5 and PM10, respectively, compared to baselines. Our method also reduced the mean absolute error of PM2.5 predictions by 20% compared to the previous state-of-the-art results on China 1-year dataset.
