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Prediction of Ozone Hourly Concentrations Based on Machine Learning Technology

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  • Dong Li

    (College of Economics and Management, Xi’an University of Posts & Telecommunications, Xi’an 710061, China)

  • Xiaofei Ren

    (College of Economics and Management, Xi’an University of Posts & Telecommunications, Xi’an 710061, China)

Abstract

To optimize the accuracy of ozone (O 3 ) concentration prediction, this paper proposes a combined prediction model of O 3 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.

Suggested Citation

  • Dong Li & Xiaofei Ren, 2022. "Prediction of Ozone Hourly Concentrations Based on Machine Learning Technology," Sustainability, MDPI, vol. 14(10), pages 1-29, May.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:10:p:5964-:d:815599
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    References listed on IDEAS

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    1. Pavitra Kumar & Sai Hin Lai & Jee Khai Wong & Nuruol Syuhadaa Mohd & Md Rowshon Kamal & Haitham Abdulmohsin Afan & Ali Najah Ahmed & Mohsen Sherif & Ahmed Sefelnasr & Ahmed El-Shafie, 2020. "Review of Nitrogen Compounds Prediction in Water Bodies Using Artificial Neural Networks and Other Models," Sustainability, MDPI, vol. 12(11), pages 1-26, May.
    2. Cao, Quoc Dung & Miles, Scott B. & Choe, Youngjun, 2022. "Infrastructure recovery curve estimation using Gaussian process regression on expert elicited data," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
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