Air Quality Prediction Based on Wavelet Analysis and Machine Learning

Authors

  • Jun Duan School of Economics & Management, Chongqing Normal University, Chongqing 401331, China
  • Qi Ren Institute of Intelligent Engineering, Chongqing City Management College, Chongqing 401331, China

DOI:

https://doi.org/10.13052/spee1048-5236.4217

Keywords:

Machine learning, weather quality, prediction

Abstract

This thesis takes the historical weather time series of Chongqing as experimental samples. Firstly, this thesis uses wavelet transform to organize the data, and then divides the sample data into training and test sets to verify the accuracy of the evaluation of the Naive Bayes Model. Secondly, the Naive Bayes Model is compared with currently used machine learning models such as SVM, XGBoost, bagging, and random forest. Finally, the results show that the Naive Bayes Model has high stability and accuracy for the air quality assessment of Chongqing, and it can be applied to the evaluation of urban ambient air quality.

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Author Biographies

Jun Duan, School of Economics & Management, Chongqing Normal University, Chongqing 401331, China

Jun Duan received a master’s degree in economics from Southwest University in 2007 and a doctor’s degree in management from Chongqing University in 2019. He has been teaching in the School of Economics and Management of Chongqing Normal University since July 2007. His current research interests include energy economy and big data mining and analysis.

Qi Ren, Institute of Intelligent Engineering, Chongqing City Management College, Chongqing 401331, China

Qi Ren received a bachelor’s degree in engineering from Zhengzhou University in 2002 and a master’s degree in agriculture from Chongqing Normal University in 2018. Since July 2012, he has been teaching at Chongqing Urban Management Vocational College, and his current research interests include machine learning and data analysis.

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Published

2022-12-28

How to Cite

Duan, J. ., & Ren, Q. . (2022). Air Quality Prediction Based on Wavelet Analysis and Machine Learning. Strategic Planning for Energy and the Environment, 42(01), 119–136. https://doi.org/10.13052/spee1048-5236.4217

Issue

Section

Green Technologies for Sustainable Environment