Evaluation and prediction of light pollution based on Topsis and BP-Grey prediction model

Rongshuo Liu; Jianing Shao; Feixue Wang; Chang Liu

doi:10.1117/12.3035464

10 July 2024 Evaluation and prediction of light pollution based on Topsis and BP-Grey prediction model

Rongshuo Liu, Jianing Shao, Feixue Wang, Chang Liu

Proceedings Volume 13223, Fifth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2024); 132231J (2024) https://doi.org/10.1117/12.3035464
Event: 2024 5th International Conference on Geology, Mapping and Remote Sensing (ICGMRS 2024), 2024, Wuhan, China

Abstract

This paper focuses on the global environmental issue of light pollution. Using remote sensing data and related side data, the light pollution risk assessment models are established for five latitudes: economy (ECO), health (HEA), environment and sustainability (SAE), climate and topography (CAT), and regional light index (RLI). First, Topsis method is used to construct a light pollution risk assessment model. In order to develop the light pollution risk coefficient system, five first-level indicators and 11 second-level indicators are identified based on the five latitudes identified above. Considering the limitation of single weight calculation method, entropy weight method and coefficient of variation method are combined to obtain comprehensive index weight. Secondly, based on the grey prediction model optimized by BP neural network, the light pollution degree of Shanghai, China and Mount Nimba, Guinea in the next 7 years is predicted, and specific optimization strategies are proposed. Finally, the robustness of the model is proved by sensitivity analysis.

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

Citation Download Citation

Rongshuo Liu, Jianing Shao, Feixue Wang, and Chang Liu "Evaluation and prediction of light pollution based on Topsis and BP-Grey prediction model", Proc. SPIE 13223, Fifth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2024), 132231J (10 July 2024); https://doi.org/10.1117/12.3035464

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