@Article{cmc.2020.010583,
AUTHOR = {Bolun Chen, Guochang Zhu, Min Ji, Yongtao Yu, Jianyang Zhao, Wei Liu},
TITLE = {Air Quality Prediction Based on Kohonen Clustering and ReliefF Feature Selection},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {64},
YEAR = {2020},
NUMBER = {2},
PAGES = {1039--1049},
URL = {http://www.techscience.com/cmc/v64n2/39344},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2020.010583}
}