Two GM (2,1) Improved Model to Predict Air Quality Index

DOI

10.2991/icamcs-16.2016.89How to use a DOI?

Keywords

Least square method to improve Dimension number replacement Changes in air quality

Abstract

At present, air pollution becomes coordinated development of Beijing-Tianjin-Hebei area major issues. This paper studies the Beijing-Tianjin-Hebei regional air quality issues. In view of the Beijing-Tianjin-Hebei 13 city air quality data, the data properly filtered, by finding and analyzing data, eventually selecting the air quality index (AQI) as a description of the air quality index. AQI complex and irregular, a day was used as observation units, AQI fluctuate in a month, but AQI slow to change in a day, so in hours on the AQI forecast. First, the GM based on least squares (2,1) model to predict the AQI, Data inspection, we find that the result of long-term forecasting model of relative error is larger. For further optimization models, creative introduction of such number of replacement method in this paper to improve the Least square method GM (2,1) model. Model shows, such as grey replacement model can improve the AQI forecast accuracy for a long time.

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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TY  - CONF
AU  - Qianwen An
PY  - 2016/06
DA  - 2016/06
TI  - Two GM (2,1) Improved Model to Predict Air Quality Index
BT  - Proceedings of the 2016 5th International Conference on Advanced Materials and Computer Science
PB  - Atlantis Press
SP  - 422
EP  - 428
SN  - 2352-5401
UR  - https://doi.org/10.2991/icamcs-16.2016.89
DO  - 10.2991/icamcs-16.2016.89
ID  - An2016/06
ER  -