Proceedings of the 2015 4th International Conference on Sensors, Measurement and Intelligent Materials

Research on Application of BP Artificial Neural Network in Prediction of the concentration of PM2.5 in Beijing

Authors
Yuanhua Chen, Lisha Wang, Lina Zhang
Corresponding Author
Yuanhua Chen
Available Online January 2016.
DOI
https://doi.org/10.2991/icsmim-15.2016.135How to use a DOI?
Keywords
BP Artificial Neural Network, prediction model, PM 2.5 concentration.
Abstract

According to the PM2.5 hourly average monitoring concentrations of Beijing environmental air monitoring stations during may and June 2014, the BP artificial neural network prediction model was built and tested, which verifies the feasibility and accuracy of BP artificial neural network model to predict PM2.5 hourly average concentration. And the forecasting results show that the reasonable structure and suitable training algorithm are the key factors to the accuracy, reliability, and consistency of PM2.5 concentrations.

Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2015 4th International Conference on Sensors, Measurement and Intelligent Materials
Series
Advances in Computer Science Research
Publication Date
January 2016
ISBN
978-94-6252-157-5
ISSN
2352-538X
DOI
https://doi.org/10.2991/icsmim-15.2016.135How to use a DOI?
Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Yuanhua Chen
AU  - Lisha Wang
AU  - Lina Zhang
PY  - 2016/01
DA  - 2016/01
TI  - Research on Application of BP Artificial Neural Network in Prediction of the concentration of PM2.5 in Beijing
BT  - Proceedings of the 2015 4th International Conference on Sensors, Measurement and Intelligent Materials
PB  - Atlantis Press
SP  - 723
EP  - 727
SN  - 2352-538X
UR  - https://doi.org/10.2991/icsmim-15.2016.135
DO  - https://doi.org/10.2991/icsmim-15.2016.135
ID  - Chen2016/01
ER  -