Optimization of urban green space in Wuhan based on machine learning algorithm from the perspective of healthy city DOI Creative Commons

Xuechun Zhou,

Xiaofei Zou,

Wenzuixiong Xiong

et al.

Frontiers in Public Health, Journal Year: 2025, Volume and Issue: 13

Published: March 6, 2025

Urban green spaces play a critical role in addressing health issues, ecological challenges, and uneven resource distribution cities. This study focuses on Wuhan, where low coverage rates imbalanced space allocation pose significant challenges. Adopting healthy city development perspective, the research aims to assess impact of optimization urban health, economic performance, social structure. A multivariable model was constructed using random forest Support Vector Machine (SVM) algorithms evaluate influence key indicators space. Core were integrated from three dimensions: residents' environmental quality, community interaction. Multiple linear regression analysis employed quantify potential benefits outcomes. The findings reveal that optimizing quality indices significantly enhances development. Green improvements drive 73% increase efficiency by improving extending life expectancy. Additionally, enhancements structure are achieved at 61% 52% through strengthened cohesion improved respectively. demonstrates high stability adaptability after multiple iterations, providing robust quantitative foundation for optimization. highlights multidimensional value promoting growth, stability. results offer solid theoretical basis practical guidance planning management cities, contributing scientific decision-making sustainable

Language: Английский

Optimization of urban green space in Wuhan based on machine learning algorithm from the perspective of healthy city DOI Creative Commons

Xuechun Zhou,

Xiaofei Zou,

Wenzuixiong Xiong

et al.

Frontiers in Public Health, Journal Year: 2025, Volume and Issue: 13

Published: March 6, 2025

Urban green spaces play a critical role in addressing health issues, ecological challenges, and uneven resource distribution cities. This study focuses on Wuhan, where low coverage rates imbalanced space allocation pose significant challenges. Adopting healthy city development perspective, the research aims to assess impact of optimization urban health, economic performance, social structure. A multivariable model was constructed using random forest Support Vector Machine (SVM) algorithms evaluate influence key indicators space. Core were integrated from three dimensions: residents' environmental quality, community interaction. Multiple linear regression analysis employed quantify potential benefits outcomes. The findings reveal that optimizing quality indices significantly enhances development. Green improvements drive 73% increase efficiency by improving extending life expectancy. Additionally, enhancements structure are achieved at 61% 52% through strengthened cohesion improved respectively. demonstrates high stability adaptability after multiple iterations, providing robust quantitative foundation for optimization. highlights multidimensional value promoting growth, stability. results offer solid theoretical basis practical guidance planning management cities, contributing scientific decision-making sustainable

Language: Английский

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