Research on Light Pollution Based on Bp Neural Network
Keywords:Light pollution, Remote sensing imagery, Gray associative model, BP neural networks.
This study focuses on the impact of local development level, population, biodiversity, geography, climate and other factors on local light pollution, establishes a widely applicable evaluation model, and reasonably evaluates the impact of various factors on light pollution. Firstly, twelve factors that may be related to light pollution are selected, grayscale processing is performed on the night remote sensing image, the color value of the grayscale image is read and quantified as the light pollution value, and the selected factors are averaged and normalized. Then, the gray correlation model is used to obtain the gray correlation degree and select the factors with strong correlation. Secondly, the light pollution evaluation model is established by the BP neural network, and the light pollution risk level is divided into four grades: A, B, C, and D, of which A grade represents the low light pollution risk level (A: 0~1, B:2~4, C:5~7, D:8~9, and the number represents the light pollution value). Based on this, this study established a set of evaluation index system to determine the level of light pollution risk level of a site. Finally, different types of locations are selected to test the established prediction model to verify the wide applicability of the established BP neural network evaluation system. The results show that the light pollution evaluation model in this study can effectively predict light pollution for different locations and has wide applicability.
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