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

Urban green spaces play a critical role in addressing health issues, ecological challenges, and uneven resource distribution in cities. This study focuses on Wuhan, where low green coverage rates and imbalanced green space allocation pose significant challenges. Adopting a healthy city development perspective, the research aims to assess the impact of green space optimization on urban health, economic performance, and social structure.

A multivariable model was constructed using random forest and Support Vector Machine (SVM) algorithms to evaluate the influence of key indicators on urban green space. Core indicators were integrated from three dimensions: residents' health, environmental quality, and community interaction. Multiple linear regression analysis was employed to quantify the potential benefits of green space optimization on economic and social outcomes.

The findings reveal that optimizing health and environmental quality indices significantly enhances green space development. Green space improvements drive a 73% increase in economic efficiency by improving residents' health and extending life expectancy. Additionally, enhancements in social structure are achieved at rates of 61% and 52% through strengthened community cohesion and improved environmental quality, respectively. The model demonstrates high stability and adaptability after multiple iterations, providing a robust quantitative foundation for green space optimization.

This study highlights the multidimensional value of green space optimization in promoting urban health, economic growth, and social stability. The results offer a solid theoretical basis and practical guidance for green space planning and management in healthy cities, contributing to scientific decision-making and sustainable urban development.