A hybrid model for analyzing the risk levels of light pollution

Tingwei Lin; Yinghao Xu; Chaoyue Chen; Yuzhe Qiu; Junrong Huang

doi:10.1117/12.3035209

21 July 2024 A hybrid model for analyzing the risk levels of light pollution

Tingwei Lin, Yinghao Xu, Chaoyue Chen, Yuzhe Qiu, Junrong Huang

Proceedings Volume 13219, Fourth International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2024); 132191E (2024) https://doi.org/10.1117/12.3035209
Event: 4th International Conference on Applied Mathematics, Modelling and Intelligent Computing (CAMMIC 2024), 2024, Kaifeng, China

Abstract

The aim of this paper is to investigate the effects of light pollution on the environment and human health, proposing effective intervention measures. A widely applicable metric is developed to determine light pollution risk levels in different regions, forming the basis for a reliable assessment model. This model, integrating Analytic Hierarchy Process (AHP), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) model, and multiple regression analysis (MRA), offers a comprehensive understanding of light pollution risk by considering regional development, light usage, and geographical factors. The assessment model demonstrates high accuracy and reliability in measuring light pollution risk, providing valuable insights into the interaction between factors such as light intensity, biodiversity loss, GDP, and population. Empirical analysis reveals regional disparities in light pollution risk levels, underscoring the need for targeted interventions to mitigate its adverse impacts. Overall, the developed light pollution risk assessment model serves as a valuable tool for policymakers, facilitating informed decision-making and effective intervention strategies.

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

Citation Download Citation

Tingwei Lin, Yinghao Xu, Chaoyue Chen, Yuzhe Qiu, and Junrong Huang "A hybrid model for analyzing the risk levels of light pollution", Proc. SPIE 13219, Fourth International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2024), 132191E (21 July 2024); https://doi.org/10.1117/12.3035209

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KEYWORDS

Pollution

Biodiversity

Data modeling

Matrices

Analytical research

Analytics

Remote sensing

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