文章基本信息

标题：Prediction of Ozone Hourly Concentrations Based on Machine Learning Technology
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作者：Dong Li ; Xiaofei Ren
期刊名称：Sustainability
印刷版ISSN：2071-1050
出版年度：2022
卷号：14
期号：10
页码：5964
DOI：10.3390/su14105964
语种：English
出版社：MDPI, Open Access Journal
摘要：To optimize the accuracy of ozone (O3) concentration prediction, this paper proposes a combined prediction model of O3 hourly concentration, FC-LsOA-KELM, which integrates multiple machine learning methods. The model has three parts. The first part is the feature construction (FC), which is based on correlation analysis and incorporates time-delay effect analysis to provide a valuable feature set. The second part is the kernel extreme learning machine (KELM), which can establish a complex mapping relationship between feature set and prediction object. The third part is the lioness optimization algorithm (LsOA), which is purposed to find the optimal parameter combination of KELM. Then, we use air pollution data from 11 cities on Fenwei Plain in China from 2 January 2015 to 30 December 2019 to test the validity of FC-LsOA-KELM and compare it with other prediction methods. The experimental results show that FC-LsOA-KELM can obtain better prediction results and has a better performance.