Open Access

ARTICLE

Air Quality Prediction Based on Kohonen Clustering and ReliefF Feature Selection

by Bolun Chen, Guochang Zhu, Min Ji, Yongtao Yu, Jianyang Zhao, Wei Liu

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.

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