Light pollution control model based on grading and optimization

Yuanyuan Wang; Cheng Peng; Yanhao Deng; Tianqi Yang; Zhiming He; Wen Li

doi:10.1117/12.3029342

22 May 2024 Light pollution control model based on grading and optimization

Yuanyuan Wang, Cheng Peng, Yanhao Deng, Tianqi Yang, Zhiming He, Wen Li

Proceedings Volume 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023); 131761K (2024) https://doi.org/10.1117/12.3029342
Event: Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 2023, Hangzhou, China

Abstract

The spread of light pollution engulfs the night sky and also has adverse effects on or-ganisms and the environment. To mitigate the light pollution problem, this paper estab-lish-es a comprehensive evaluation model of light pollution degree by optimizing TOPSIS model based on the combination of AHP and EWM. After that, a policy effect maximization model based on 0-1 planning is established and the optimal solution policy is solved.A comprehensive evaluation model of light pollution level was es-tablished. To widely measure the regional light pollution level, six effective indicators were selected to evaluate the light pollution level in different regions, namely: Level of devel-opment, Population, Air quality, Night light intensity, Climate, Geography. Matlab was used to calculate the weights of the selected indicators and then combined with TOPSIS scores to evaluate the regional light pollution.The highlight of this paper is the combination of subjective and objective. Using AHP and EWM to solve the weights makes the model more convincing. Finally, the model was evaluated with R2 and F-tests, and the model fit reached more than 95%.

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

Citation Download Citation

Yuanyuan Wang, Cheng Peng, Yanhao Deng, Tianqi Yang, Zhiming He, and Wen Li "Light pollution control model based on grading and optimization", Proc. SPIE 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 131761K (22 May 2024); https://doi.org/10.1117/12.3029342

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