Air quality analysis of major Chinese cities based on semi-supervised clustering

He Huang; Zuhan Liu; Lili Wang; Xin Huang

doi:10.1117/12.3009272

11 October 2023 Air quality analysis of major Chinese cities based on semi-supervised clustering

He Huang, Zuhan Liu, Lili Wang, Xin Huang

Proceedings Volume 12918, Fourth International Conference on Computer Science and Communication Technology (ICCSCT 2023); 129180D (2023) https://doi.org/10.1117/12.3009272
Event: International Conference on Computer Science and Communication Technology (ICCSCT 2023), 2023, Wuhan, China

Abstract

In recent years, the deteriorating atmospheric quality in major Chinese cities has posed significant threats to residents’ health. Effective analysis and assessment are crucial for addressing this issue. However, due to the lack of sufficient labeled data, conventional supervised learning methods often face challenges in application. Therefore, this study aims to analyze the atmospheric quality in major Chinese cities using a semi-supervised clustering approach. We collected five years of air quality data from major Chinese cities, including AQI, PM_2.5, PM₁₀, SO₂, NO₂, CO, and O₃. We applied the semi-supervised clustering method to cluster the data. Finally, we evaluated the model using the decision tree algorithm and calculated the accuracy of the model to be 89.28%. The findings indicate that the semi-supervised clustering approach is an effective method to gain deeper insights into the variations in air quality in major Chinese cities and to provide data support for formulating relevant policies.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

He Huang, Zuhan Liu, Lili Wang, and Xin Huang "Air quality analysis of major Chinese cities based on semi-supervised clustering", Proc. SPIE 12918, Fourth International Conference on Computer Science and Communication Technology (ICCSCT 2023), 129180D (11 October 2023); https://doi.org/10.1117/12.3009272

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