Paper Info
Title
An Attention-Based Air Quality Forecasting Method
Abstract
Air pollution is threatening human's health since the industrial revolution, but there are not efficient ways to solve air pollution, so forecasting air quality has become an efficient measure to prevent citizens from hurting of heavy air pollution. In this paper, we proposed an advanced Seq2Seq (Sequence to Sequence) model called attention-based air quality forecasting model (ABAFM) whose RNN encoder is replaced by pure attention mechanism with position embedding. This improvement not only reduces the training time of Seq2Seq model with attention but also enhances the robustness of Seq2Seq models. We implemented ABAFM in Olympic center and Dongsi monitoring stations in Beijing to forecast PM2.5 in future 24 hours. The experimental results showed that the proposed model outperformed the related arts, especially in sudden changes.
Year
DOI
Venue
2018
10.1109/ICMLA.2018.00115
2018 17TH IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA)
Keywords
Field
DocType
Air quality, forecasting, Seq2Seq, Attention
Computer science,Operations research,Robustness (computer science),Atmospheric model,Air quality index,Artificial intelligence,Encoder,Air pollution,Machine learning,Beijing
Conference
Citations 
PageRank 
References 
0
0.34
0
Authors
7
Name
Order
Citations
PageRank
Bo Liu152184.67
Shuo Yan2247.61
Jianqiang Li315619.55
Guangzhi Qu418925.23
Yong Li500.68
Jianlei Lang611.72
Rentao Gu7258.24