Article
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Light Pollution Index System Model Based on Markov Random Field
Version 1
: Received: 21 June 2023 / Approved: 22 June 2023 / Online: 22 June 2023 (11:48:40 CEST)
A peer-reviewed article of this Preprint also exists.
Fang, L.; Wu, Z.; Tao, Y.; Gao, J. Light Pollution Index System Model Based on Markov Random Field. Mathematics 2023, 11, 3030. Fang, L.; Wu, Z.; Tao, Y.; Gao, J. Light Pollution Index System Model Based on Markov Random Field. Mathematics 2023, 11, 3030.
Abstract
In this paper, a Markov random field model is proposed to determine a site’s light pollution risk level. The specific data of 12 indicators of five typical cities in China is first collected to establish a hierarchical indicator system using an R-type clustering algorithm. Then, the entropy weight method is used to filter, determine 10-factor indicators, three potential impact indicators, and a light pollution risk index, and establish an undirected probability map model. A light pollution measurement based on Markov random field is obtained, and a location-based light pollution risk assessment index (LBLPRAI) is proposed. LBLPRAI of different types of sites is analyzed, and three possible intervention strategies are proposed to solve the light pollution problem: road lighting system planning, increasing vegetation coverage, and building system planning. Finally, the simulated annealing algorithm is used to determine the best intervention strategy, and it is concluded that the use of strategy 1 in the urban community 2 is the most effective measure, which can reduce the risk level of light pollution by 17.2%.
Keywords
light pollution; entropy weight method; Markov random fields; simulated annealing algorithm
Subject
Computer Science and Mathematics, Applied Mathematics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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