当前位置: X-MOL 学术Connect. Sci. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Self-adaptive spatial-temporal network based on heterogeneous data for air quality prediction
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
Affiliation  

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。

更新日期:2020-11-23
down
wechat
bug