Improving summer outdoor comfort in metropolitan park: a data-driven approach using AI, experimental and design analysis DOI Open Access

T.-H. Chen,

Chien-Shiun Huang,

Wen‐Pei Sung

и другие.

Journal of Measurements in Engineering, Год журнала: 2025, Номер unknown

Опубликована: Янв. 2, 2025

This study aims to address the growing urban heat challenges by exploring application of AI-driven simulations improve outdoor thermal comfort and air quality in parks. The primary goal was optimize park designs using advanced AI technologies data analysis, improving public green spaces. A highly accurate model employed, with performance metrics including RMSE (3.68 °C), MAPE (6.50 %), a Pearson Correlation Coefficient 0.982, evaluate key environmental parameters such as temperature, wind speed, radiation. These assessments served foundation for design optimization through integration Computational Fluid Dynamics (CFD) modeling. Innovative improvements, enhanced shading structures, strategic vegetation placement, refined material selection, resulted 15 % reduction radiation, 1 m/s increase decrease PM 2.5 10 concentrations 12 %, respectively. changes led increased pedestrian comfort, improved health outcomes, 20 rise usage. Post-optimization analysis further demonstrated 25 radiation improvement Air Quality Index (AQI). Furthermore, resilience testing short-term climate indicated that these improvements would remain effective at least three years, confirming robustness long-term sustainability AI-enhanced strategies. research highlights potential integrating design, offering valuable insights into creating sustainable, user-centered By combining real-world optimization, emphasizes importance interdisciplinary approaches enhancing livability environments.

Язык: Английский

Improving summer outdoor comfort in metropolitan park: a data-driven approach using AI, experimental and design analysis DOI Open Access

T.-H. Chen,

Chien-Shiun Huang,

Wen‐Pei Sung

и другие.

Journal of Measurements in Engineering, Год журнала: 2025, Номер unknown

Опубликована: Янв. 2, 2025

This study aims to address the growing urban heat challenges by exploring application of AI-driven simulations improve outdoor thermal comfort and air quality in parks. The primary goal was optimize park designs using advanced AI technologies data analysis, improving public green spaces. A highly accurate model employed, with performance metrics including RMSE (3.68 °C), MAPE (6.50 %), a Pearson Correlation Coefficient 0.982, evaluate key environmental parameters such as temperature, wind speed, radiation. These assessments served foundation for design optimization through integration Computational Fluid Dynamics (CFD) modeling. Innovative improvements, enhanced shading structures, strategic vegetation placement, refined material selection, resulted 15 % reduction radiation, 1 m/s increase decrease PM 2.5 10 concentrations 12 %, respectively. changes led increased pedestrian comfort, improved health outcomes, 20 rise usage. Post-optimization analysis further demonstrated 25 radiation improvement Air Quality Index (AQI). Furthermore, resilience testing short-term climate indicated that these improvements would remain effective at least three years, confirming robustness long-term sustainability AI-enhanced strategies. research highlights potential integrating design, offering valuable insights into creating sustainable, user-centered By combining real-world optimization, emphasizes importance interdisciplinary approaches enhancing livability environments.

Язык: Английский

Процитировано

1