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Modeling the Effect of Greenways’ Multilevel Visual Characteristics on Thermal Perception in Summer Based on Bayesian Network and Computer Vision

Author

Listed:
  • Yongrong Zheng

    (College of Landscape Architecture and Art, Fujian Agriculture and Forestry University, Fuzhou 350002, China)

  • Siren Lan

    (College of Landscape Architecture and Art, Fujian Agriculture and Forestry University, Fuzhou 350002, China)

  • Jiayi Zhao

    (College of Landscape Architecture and Art, Fujian Agriculture and Forestry University, Fuzhou 350002, China)

  • Yuhan Liu

    (College of Landscape Architecture and Art, Fujian Agriculture and Forestry University, Fuzhou 350002, China)

  • Songjun He

    (College of Landscape Architecture and Art, Fujian Agriculture and Forestry University, Fuzhou 350002, China)

  • Chang Liu

    (College of Landscape Architecture and Art, Fujian Agriculture and Forestry University, Fuzhou 350002, China)

Abstract

The aim of this study is to reveal the effects of multilevel visual characteristics of greenways on thermal perception in hot and humid regions during summer and to explore the potential of visual design to enhance psychological thermal comfort. Data on light (L), color (C), plant richness (PR), space openness (SO), scenic view (SV), thermal sensation (TS), and thermal preference (TP) were collected through questionnaires ( n = 546). Computer vision technology was applied to measure the green view index (GVI), sky view index (SVI), paving index (PI), spatial enclosure (SE), and water index (WI). Using the hill climbing algorithm in R to construct a Bayesian network, model validation results indicated prediction accuracies of 0.799 for TS and 0.838 for TP. The results showed that: (1) SE, WI, and SV significantly positively influence TS, while L significantly negatively influences TS (R 2 = 0.6805, p -value < 0.05); (2) WI, TS, and SV significantly positively influence TP (R 2 = 0.759, p -value < 0.05).

Suggested Citation

  • Yongrong Zheng & Siren Lan & Jiayi Zhao & Yuhan Liu & Songjun He & Chang Liu, 2024. "Modeling the Effect of Greenways’ Multilevel Visual Characteristics on Thermal Perception in Summer Based on Bayesian Network and Computer Vision," Land, MDPI, vol. 13(11), pages 1-27, October.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:11:p:1796-:d:1511113
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    References listed on IDEAS

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    1. Fuhao Sun & Junhua Zhang & Ruochen Yang & Shuhao Liu & Jia Ma & Xiaoke Lin & Daer Su & Kun Liu & Jingshu Cui, 2023. "Study on Microclimate and Thermal Comfort in Small Urban Green Spaces in Tokyo, Japan—A Case Study of Chuo Ward," Sustainability, MDPI, vol. 15(24), pages 1-19, December.
    2. Doguc, Ozge & Ramirez-Marquez, Jose Emmanuel, 2009. "A generic method for estimating system reliability using Bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 542-550.
    3. Xuefei Wang & Zhiqi Chen & Dawei Ma & Tingting Zhou & Jintang Chen & Xing Jiang, 2023. "Relationship between Visual and Thermal Comfort and Electrodermal Activity in Campus Blue–Green Spaces: A Case Study of Guangzhou, China," Sustainability, MDPI, vol. 15(15), pages 1-20, July.
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