Air Quality Prediction Based on Kohonen Clustering and ReliefF Feature Selection  

作　　者：Bolun Chen Guochang Zhu Min Ji Yongtao Yu Jianyang Zhao Wei Liu 

机构地区：[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

出　　处：《Computers, Materials & Continua》2020年第8期1039-1049,共11页计算机、材料和连续体（英文）

基　　金：This research was supported in part by the National Natural Science Foundation of China under grant Nos.61602202 and 61603146;the Natural Science Foundation of Jiangsu Province under contracts BK20160428 and BK20160427;the Six talent peaks project in Jiangsu Province under contract XYDXX-034;the project in Jiangsu Association for science and technology.

摘　　要：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.

关 键 词：Air quality prediction Kohonen clustering ReliefF feature selection 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]