A light pollution risk intervention strategy optimization model based on improved PCA clustering algorithm

Ruohong Yang; Jiahui Qiu; Ainuo Liu; Jianjia Wang

doi:10.1117/12.3006062

10 October 2023 A light pollution risk intervention strategy optimization model based on improved PCA clustering algorithm

Ruohong Yang, Jiahui Qiu, Ainuo Liu, Jianjia Wang

Author Affiliations +

Proceedings Volume 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023); 1279907 (2023) https://doi.org/10.1117/12.3006062
Event: 3rd International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 2023, Kuala Lumpur, Malaysia

Abstract

Artificial light sources have a profound impact on the night sky, leading to reduced visibility of stars and contributing to light pollution. The environmental, physiological, and psychological consequences of light pollution are severe, including disrupting ecosystems, biological clocks, and endocrine imbalances in humans. Moreover, excessive lighting also wastes energy and exacerbates the energy crisis. This paper collects eight light pollution indicators and proposes a general measurement model for light pollution risk level using an improved PCA clustering algorithm. This paper also proposes intervention strategy optimization model to reduce the risk level, and evaluates the effectiveness of these interventions in Shanghai. Overall, this paper provides insights into the causes and consequences of light pollution, and practical solutions for mitigating its harmful effects.

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

Citation Download Citation

Ruohong Yang, Jiahui Qiu, Ainuo Liu, and Jianjia Wang "A light pollution risk intervention strategy optimization model based on improved PCA clustering algorithm", Proc. SPIE 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 1279907 (10 October 2023); https://doi.org/10.1117/12.3006062

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