Prediction of Ozone Hourly Concentrations Based on Machine Learning Technology

Sustainability

Dong Li¹  Xiaofei Ren¹ 

[1] College of Economics and Management, Xi’an University of Posts & Telecommunications, Xi’an 710061, China;

关键词: O₃;    prediction;    lioness optimization algorithm;    kernel extreme learning machine;   

DOI  :  10.3390/su14105964

来源: DOAJ

【 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.

【 授权许可】

Unknown