Haze prediction research based on SSA-SVR

Kexin Zhao; Zuhan Liu; Lili Wang; Yun Peng

doi:10.1117/12.3009266

11 October 2023 Haze prediction research based on SSA-SVR

Kexin Zhao, Zuhan Liu, Lili Wang, Yun Peng

Proceedings Volume 12918, Fourth International Conference on Computer Science and Communication Technology (ICCSCT 2023); 129180F (2023) https://doi.org/10.1117/12.3009266
Event: International Conference on Computer Science and Communication Technology (ICCSCT 2023), 2023, Wuhan, China

Abstract

In response to the increasing severity of hazy weather and the difficulty of prediction, an SSA-SVR based PM2.5 content prediction method is proposed. Specifically, the excellent search capability of the Sparrow Search Algorithm (SSA) was utilized to search for the optimal parameter combinations for the Support Vector Regression (SVR) machine. Firstly, the meteorological factors are dimensionalized using factor analysis. Then the prediction effect of PM_2.5 in Beijing is experimentally compared with the regression model constructed by other algorithms. The results show that SSA has stable global search performance and can effectively reduce the influence of SVR parameter selection on the generalization ability and regression accuracy of the system. This is useful for monitoring and prevention of haze and so on.

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

Citation Download Citation

Kexin Zhao, Zuhan Liu, Lili Wang, and Yun Peng "Haze prediction research based on SSA-SVR", Proc. SPIE 12918, Fourth International Conference on Computer Science and Communication Technology (ICCSCT 2023), 129180F (11 October 2023); https://doi.org/10.1117/12.3009266

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