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Light Pollution Index System Model Based on Markov Random Field

Author

Listed:
  • Liangkun Fang

    (School of Science, Zhejiang Sci-Tech University, Hangzhou 310018, China)

  • Zhangjie Wu

    (School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China)

  • Yuan Tao

    (School of Economics and Management, Zhejiang Sci-Tech University, Hangzhou 310018, China)

  • Jinfeng Gao

    (School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China)

Abstract

Light pollution is one of the environmental pollution problems facing the world. The research on the measurement standard of light pollution is not perfect at present. In this paper, we proposed a Markov random field model to determine the light pollution risk level of a site. Firstly, the specific data of 12 indicators of 5 typical cities were collected, and 10-factor indicators were screened using the R-type clustering algorithm. Then, the entropy weight method was used to determine the weight, and the light pollution measurement method of the Markov random field was established. The model was tested by five different data sets, and the test results show that the model is very effective. Three kinds of potential effects were proposed, and the relationship between the factor index and potential effects was established by using the partial least square method. Three possible intervention strategies for solving the problem of light pollution are pointed out: road lighting system planning, increasing vegetation coverage, and building system planning. Finally, a simulated annealing algorithm was used to determine the best intervention strategy, concluding that using strategy 1 in urban neighborhood 2 was the most effective measure, reducing the risk level of light pollution by 17.2%.

Suggested Citation

  • Liangkun Fang & Zhangjie Wu & Yuan Tao & Jinfeng Gao, 2023. "Light Pollution Index System Model Based on Markov Random Field," Mathematics, MDPI, vol. 11(13), pages 1-18, July.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:13:p:3030-:d:1189214
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    References listed on IDEAS

    as
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    4. Yong-Chao Su & Cheng-Yu Wu & Cheng-Hong Yang & Bo-Sheng Li & Sin-Hua Moi & Yu-Da Lin, 2021. "Machine Learning Data Imputation and Prediction of Foraging Group Size in a Kleptoparasitic Spider," Mathematics, MDPI, vol. 9(4), pages 1-16, February.
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