Air Quality Prediction Based on Wavelet Analysis and Machine Learning

被引：0

作者：

Duan J. ^{[1
]}

Ren Q. ^{[2
]}

机构：

[1] School of Economics & Management, Chongqing Normal University, Chongqing

[2] Institute of Intelligent Engineering, Chongqing City Management College, Chongqing

来源：

Strategic Planning for Energy and the Environment | 2023年 / 42卷 / 01期

基金：

中国国家自然科学基金;

关键词：

Machine learning; prediction; weather quality;

D O I：

10.13052/spee1048-5236.4217

中图分类号：

学科分类号：

摘要：

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. © 2022 River Publishers.

引用

页码：119 / 136

页数：17

共 25 条

[1]

Onkal-Engin G., Demir I., Hiz H., Assessment of urban air quality in Istanbul using fuzzy synthetic evaluation, Atmospheric Environment, 38, 23, pp. 3809-3815, (2004)

[2]

Chan S. C., Lee C. T., Evaluation of the temporal variations of air quality in Taipei City, Taiwan, from 1994 to 2003, Journal of Environmental Management, 86, 4, pp. 627-635, (2008)

[3]

Zhang H. R., Yin X. L., Study on the assessment of atmospheric environment quality in Qingdao from 2004 to 2013 based on AHP, Environmental Science and Management, 40, 7, pp. 180-184, (2015)

[4]

Li Z. Y., The method of composite index of environmental quality, Environmental Science in China, 6, pp. 75-77, (1997)

[5]

Zhang G. Q., Evaluation of ambient air quality in Golmud City by matter element analysis, Qinghai Environment, 2, pp. 78-80, (1997)

[6]

Liu Y., Nie J., Li X., Ahmed S. H., Lim W. Y., Miao C., Federated Learning in the Sky: Aerial-Ground Air Quality Sensing Framework with UAV Swarms, IEEE Internet of Things Journal, (2020)

[7]

Ahilan A., Et al., Segmentation by Fractional Order Darwinian Particle Swarm Optimization Based Multilevel Thresholding and Improved Lossless Prediction Based Compression Algorithm for Medical Images, IEEE Access, 7, pp. 89570-89580, (2019)

[8]

Li Y. M., Econometric analysis of influencing factors of air quality in Beijing, Theoretical Discussion, 5, pp. 260-261, (2011)

[9]

Wu H. J., He X. H., Prediction of air quality index based on GA-BP neural network, Journal of Anhui Normal University (Natural Science Edition), 42, 4, pp. 360-365, (2019)

[10]

Liu L., Application of circulation neural network model based on TensorFlow in air quality prediction of Shanghai, (2019)

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