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作者:

Liu, Bo (Liu, Bo.) (学者:刘博) | Yan, Shuo (Yan, Shuo.) | Li, Jianqiang (Li, Jianqiang.) (学者:李建强) | Li, Yong (Li, Yong.) | Lang, Jianlei (Lang, Jianlei.) (学者:郎建垒) | Qu, Guangzhi (Qu, Guangzhi.)

收录:

SCIE

摘要:

With rapid industrial development, air pollution problems, especially in urban and metropolitan centers, have become a serious societal problem and require our immediate attention and comprehensive solutions to protect human and animal health and the environment. Because bad air quality brings prominent effects on our daily life, how to forecast future air quality accurately and tenuously has emerged as a priority for guaranteeing the quality of human life in many urban areas worldwide. Existing models usually neglect the influence of wind and do not consider both distance and similarity to select the most related stations, which can provide significant information in prediction. Therefore, we propose a Geographic Self-Organizing Map (GeoSOM) spatiotemporal gated recurrent unit (GRU) model, which clusters all the monitor stations into several clusters by geographical coordinates and time-series features. For each cluster, we build a GRU model and weighted different models with the Gaussian vector weights to predict the target sequence. The experimental results on real air quality data in Beijing validate the superiority of the proposed method over a number of state-of-the-art ones in metrics, such as R-2, mean relative error (MRE), and mean absolute error (MAE). The MAE, MRE, and R-2 are 16.1, 0.79, and 035 at the Gucheng station and 19.53, 0.82, and 036 at the Dongsi station.

关键词:

Air quality environment pollution prediction recurrent neural network (RNN) spatiotemporal sequences

作者机构:

  • [ 1 ] [Liu, Bo]Beijing Univ Technol, Fac Informat Technol, Sch Software Engn, Beijing, Peoples R China
  • [ 2 ] [Li, Jianqiang]Beijing Univ Technol, Fac Informat Technol, Sch Software Engn, Beijing, Peoples R China
  • [ 3 ] [Li, Yong]Beijing Univ Technol, Fac Informat Technol, Sch Software Engn, Beijing, Peoples R China
  • [ 4 ] [Liu, Bo]Univ Auckland, Sch Comp Sci, Beijing 1010, Peoples R China
  • [ 5 ] [Yan, Shuo]Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
  • [ 6 ] [Lang, Jianlei]Beijing Univ Technol, Coll Environm & Energy Engn, Key Lab Beijing Reg Air Pollut Control, Beijing 100124, Peoples R China
  • [ 7 ] [Qu, Guangzhi]Oakland Univ, Comp Sci & Engn Dept, Rochester, MI 48309 USA

通讯作者信息:

  • [Yan, Shuo]Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China

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来源 :

IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS

ISSN: 2329-924X

年份: 2021

期: 3

卷: 8

页码: 578-588

5 . 0 0 0

JCR@2022

被引次数:

WoS核心集被引频次: 26

SCOPUS被引频次: 29

ESI高被引论文在榜: 0 展开所有

万方被引频次:

中文被引频次:

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