Connection Science ( IF 5.3 ) Pub Date : 2020-11-23 , DOI: 10.1080/09540091.2020.1841095 Feng Chang 1, 2 , Liang Ge 1, 2 , Siyu Li 1, 2 , Kunyan Wu 1, 2 , Yaqian Wang 1, 2
With the development of society and the rise of people's environmental awareness, air pollution is receiving increased public attention. Accurate air quality prediction can provide useful information for government decision-making and residents' activities. However, accurately predicting future air quality remains a challenging task because of the complex spatial-temporal dependencies of air quality. Previous studies failed to explicitly model these spatial-temporal dependencies. In this paper, we propose a self-adaptive spatial-temporal network (SA-STNet) to efficiently and effectively capture the spatial-temporal dependencies of air quality. In order to effectively aggregate spatial information, we employ a self-adaptive graph convolution module that can learn the latent spatial correlations of air quality automatically. In the temporal dimension, we utilise three independent components to capture the recent, daily-periodic, and weekly-periodic temporal dependencies of air quality, respectively. In addition, our model exploits rich external complementary information by means of a features extraction component. A parametric-matrix-based fusion architecture is used to combine the outputs of different components into a joint representation which is used for generating the final prediction results. Extensive experiments carried out on real-world datasets demonstrate the outstanding performance of our model compared with baselines and state-of-the-art methods.
中文翻译:
基于异构数据的自适应时空网络空气质量预测
随着社会的发展和人们环保意识的提高,空气污染越来越受到公众的关注。准确的空气质量预测可以为政府决策和居民活动提供有用的信息。然而,由于空气质量的复杂时空依赖性,准确预测未来的空气质量仍然是一项具有挑战性的任务。以前的研究未能明确地对这些时空依赖性进行建模。在本文中,我们提出了一种自适应时空网络(SA-STNet)来有效地捕捉空气质量的时空依赖性。为了有效地聚合空间信息,我们采用了自适应图卷积模块,可以自动学习空气质量的潜在空间相关性。在时间维度上,我们利用三个独立的组件来分别捕获空气质量的近期、每日周期和每周周期的时间依赖性。此外,我们的模型通过特征提取组件利用了丰富的外部补充信息。基于参数矩阵的融合架构用于将不同组件的输出组合成联合表示,用于生成最终预测结果。在真实世界数据集上进行的大量实验证明,与基线和最先进的方法相比,我们的模型具有出色的性能。我们的模型通过特征提取组件利用丰富的外部补充信息。基于参数矩阵的融合架构用于将不同组件的输出组合成联合表示,用于生成最终预测结果。在真实世界数据集上进行的大量实验证明,与基线和最先进的方法相比,我们的模型具有出色的性能。我们的模型通过特征提取组件利用丰富的外部补充信息。基于参数矩阵的融合架构用于将不同组件的输出组合成联合表示,用于生成最终预测结果。在真实世界数据集上进行的大量实验证明,与基线和最先进的方法相比,我们的模型具有出色的性能。