Semi-supervised clustering of PM2.5 pollution

Chaonan Zhu; Zuhan Liu

doi:10.1117/12.3025598

8 April 2024 Semi-supervised clustering of PM_2.5 pollution

Chaonan Zhu, Zuhan Liu

Proceedings Volume 13090, International Conference on Computer Application and Information Security (ICCAIS 2023); 130901Q (2024) https://doi.org/10.1117/12.3025598
Event: International Conference on Computer Application and Information Security (ICCAIS 2023), 2023, Wuhan, China

Abstract

Air pollution is one of the serious problems facing China at present. The study of air quality in major cities in China is of great significance for environmental protection and air pollution control. In this paper, the semi-supervised clustering method is used to analyze the air quality in Nanchang City, Jiangxi Province, which provides a new research idea for air pollution and environmental protection in Nanchang City. Firstly, the data are preprocessed and feature extracted. Secondly, the semi-supervised K-means algorithm is used to cluster the data. Finally, the clustering results were evaluated and analyzed by visualization and statistical analysis. Based on the daily average content of atmospheric PM_2.5 in 9 locations in Nanchang in 2021, different types of PM_2.5 were obtained by elbow analysis method, and the characteristics of PM_2.5 pollution in different regions of Nanchang were studied and analyzed by fast K-means clustering technology. The results show that the seasonal variation of PM_2.5 concentration in Nanchang City is very significant, and the overall characteristics are high in winter, low in spring, and low in summer and autumn.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Chaonan Zhu and Zuhan Liu "Semi-supervised clustering of PM_2.5 pollution", Proc. SPIE 13090, International Conference on Computer Application and Information Security (ICCAIS 2023), 130901Q (8 April 2024); https://doi.org/10.1117/12.3025598

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