Open Access
ARTICLE
Air Quality Prediction Based on Kohonen Clustering and ReliefF Feature Selection
Bolun Chen1, 2, Guochang Zhu1, *, Min Ji1, Yongtao Yu1, Jianyang Zhao1, Wei Liu3
1 College of Computer Engineering, Huaiyin Institute of Technology, Huaian, 233003, China.
2 Department of Physics, University of Fribourg, Fribourg, CH-1700, Switzerland.
3 College of Information Engineering, Yangzhou University, Yangzhou, 225009, China.
* Corresponding Author: Guochang Zhu. Email: .
Computers, Materials & Continua 2020, 64(2), 1039-1049. https://doi.org/10.32604/cmc.2020.010583
Received 12 March 2020; Accepted 16 April 2020; Issue published 10 June 2020
Abstract
Air quality prediction is an important part of environmental governance. The
accuracy of the air quality prediction also affects the planning of people’s outdoor
activities. How to mine effective information from historical data of air pollution and
reduce unimportant factors to predict the law of pollution change is of great significance
for pollution prevention, pollution control and pollution early warning. In this paper, we
take into account that there are different trends in air pollutants and that different climatic
factors have different effects on air pollutants. Firstly, the data of air pollutants in
different cities are collected by a sliding window technology, and the data of different
cities in the sliding window are clustered by Kohonen method to find the same tends in
air pollutants. On this basis, combined with the weather data, we use the ReliefF method
to extract the characteristics of climate factors that helpful for prediction. Finally,
different types of air pollutants and corresponding extracted the characteristics of climate
factors are used to train different sub models. The experimental results of different
algorithms with different air pollutants show that this method not only improves the
accuracy of air quality prediction, but also improves the operation efficiency.
Keywords
Cite This Article
B. Chen, G. Zhu, M. Ji, Y. Yu, J. Zhao
et al., "Air quality prediction based on kohonen clustering and relieff feature selection,"
Computers, Materials & Continua, vol. 64, no.2, pp. 1039–1049, 2020. https://doi.org/10.32604/cmc.2020.010583