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Author:

Li, X. (Li, X..) | Mei, J. (Mei, J..) | Zhang, S. (Zhang, S..)

Indexed by:

Scopus PKU CSCD

Abstract:

In order to improve the prediction accuracy of the atmospheric pollutants concentration, the gray correlation analysis is used to select the main factors affecting the PM2.5 concentration in the atmosphere. Regarding them as the input variables, a model based on BP_Adaboost neural network is proposed to predict the PM2.5 concentration. The modified particle swarm optimization (MPSO) algorithm is applied to choose the weight and threshold of BP_Adaboost neural network, which can availably avoid falling into local optimal solution. According to the concentration of air pollutants and meteorological condition, the data between November 1, 2014 to November 25, 2014 and July 7, 2017 to August 6, 2017, which are monitored every hour by the Wanliu station of Haidian distinct and Beijing University of Technology point of Chaoyang distinct in Beijing, are used as the experiment object. The simulation results show that the PM2.5 concentration prediction performance of MPSO-BP_Adaboost neural network is better than that of BP_Adaboost, BP and generalized regression neural network. © 2018, Editorial Office of Journal of Dalian University of Technology. All right reserved.

Keyword:

BP_Adaboost neural network; Gray correlation analysis; Modified particle swarm optimization (MPSO) algorithm; PM2.5 concentration prediction model

Author Community:

  • [ 1 ] [Li, X.]Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
  • [ 2 ] [Li, X.]Beijing Key Laboratory of Computational Intelligence and Intelligent Systems, Beijing, 100124, China
  • [ 3 ] [Li, X.]Engineering Research Center of Digital Community, Ministry of Education, Beijing, 100124, China
  • [ 4 ] [Mei, J.]Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
  • [ 5 ] [Zhang, S.]Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China

Reprint Author's Address:

  • [Li, X.]Faculty of Information Technology, Beijing University of TechnologyChina

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Source :

Journal of Dalian University of Technology

ISSN: 1000-8608

Year: 2018

Issue: 3

Volume: 58

Page: 316-323

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 1

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 6

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