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Visualization system of Hlai ethnic village landscape design based on machine learning

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Abstract

Ethnic villages are an important part of villages, and also the inheritance of ethnic, cultural and spiritual forms. In a certain sense, the overall landscape characteristics of ethnic villages are the epitome of a national culture and image. In this paper, a landscape visualization scheme based on machine learning is proposed by combining the computing form of machine learning with the current visualization system. And use the principle of logistic regression and the principle of support vector calculation to analyze, select the appropriate data set for trend data analysis. Finally, taking the Hlai ethnic village landscape as the research direction in Hainan, the research is carried out in the fields of landscape science and ecology, and the comprehensive analysis is carried out in combination with the national culture, social, ecological and esthetic sensibility, and the Hlai ethnic village's own landscape index system is established. The research shows that the visual system research on the Hlai ethnic village landscape can reflect the Hlai ethnic village cultural landscape to a certain extent and is helpful to the protection and innovation of the Hlai ethnic villages; It is helpful to establish the landscape classification of Hlai ethnic villages and the establishment of future goals; To create their own national cultural landscape for Hlai ethnic villages; At the same time, it helps the sustainable development of Hlai ethnic villages and provides practical methods for the construction of Hlai ethnic villages.

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Funding

This paper was supported by (1) Education Department of Hainan Province, project number: Hnky2020-9; (2) Philosophy and Social Science Planning Project of Hainan Province, project number: HNSK(ZC)21-158.

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Correspondence to Li Wang.

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Liu, J., Wu, X., Zhang, Y. et al. Visualization system of Hlai ethnic village landscape design based on machine learning. Soft Comput 27, 10001–10011 (2023). https://doi.org/10.1007/s00500-023-08196-8

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