Revealing the driving factors of urban wetland park cooling effects using Random Forest regression and SHAP algorithm
•Utilized RF-SHAP to explore the driving factors of park cooling effects.•Percentage of water bodies inside parks is critical in cooling intensity.•Park area is the most important driving factor on cooling efficiency.•The interactions between driving factors are important for cooling effects. Urban...
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| Published in | Sustainable cities and society Vol. 120; p. 106151 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
15.02.2025
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| Subjects | |
| Online Access | Get full text |
| ISSN | 2210-6707 |
| DOI | 10.1016/j.scs.2025.106151 |
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| Abstract | •Utilized RF-SHAP to explore the driving factors of park cooling effects.•Percentage of water bodies inside parks is critical in cooling intensity.•Park area is the most important driving factor on cooling efficiency.•The interactions between driving factors are important for cooling effects.
Urban wetland parks (UWP) can effectively mitigate urban extreme heat events in an environmentally friendly manner. Most studies ignored the interactions among the driving factors on the cooling effects of UWP. In this study, we integrated Landsat 8 imagery, Random Forest (RF) regression and SHapley Additive exPlanations (SHAP) algorithm to comprehensively investigate the cooling effects of 477 UWP across China from cooling intensity and efficiency perspectives, and to reveal the interactions of driving factors. Interaction between vegetation outside UWP and temperature had the strongest effect on UWPCI, while park area and park perimeter had the strongest effect on UWPCE. A low percentage of water body inside UWP (< 20%) could increase cooling intensity when percentage of impervious land outside UWP is greater than 50%. On average, the interactions between driving factors account for 92.15% and 129.01% of the direct influence on UWPCI and UWPCE, respectively. From the perspective of cooling efficiency, the optimal park area is 1.25 km2. This findings contribute to a deeper understanding of the driving factors behind the cooling effects of UWP and better serve UWP planning to mitigate urban extreme heat events. |
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| AbstractList | •Utilized RF-SHAP to explore the driving factors of park cooling effects.•Percentage of water bodies inside parks is critical in cooling intensity.•Park area is the most important driving factor on cooling efficiency.•The interactions between driving factors are important for cooling effects.
Urban wetland parks (UWP) can effectively mitigate urban extreme heat events in an environmentally friendly manner. Most studies ignored the interactions among the driving factors on the cooling effects of UWP. In this study, we integrated Landsat 8 imagery, Random Forest (RF) regression and SHapley Additive exPlanations (SHAP) algorithm to comprehensively investigate the cooling effects of 477 UWP across China from cooling intensity and efficiency perspectives, and to reveal the interactions of driving factors. Interaction between vegetation outside UWP and temperature had the strongest effect on UWPCI, while park area and park perimeter had the strongest effect on UWPCE. A low percentage of water body inside UWP (< 20%) could increase cooling intensity when percentage of impervious land outside UWP is greater than 50%. On average, the interactions between driving factors account for 92.15% and 129.01% of the direct influence on UWPCI and UWPCE, respectively. From the perspective of cooling efficiency, the optimal park area is 1.25 km2. This findings contribute to a deeper understanding of the driving factors behind the cooling effects of UWP and better serve UWP planning to mitigate urban extreme heat events. |
| ArticleNumber | 106151 |
| Author | Jiang, Weiguo Deng, Yue Ling, Ziyan Sun, Shujuan Liu, Lulu |
| Author_xml | – sequence: 1 givenname: Yue surname: Deng fullname: Deng, Yue email: dengyue@cdu.edu.cn organization: School of Architecture and Civil Engineering, Chengdu University, Chengdu 610106, China – sequence: 2 givenname: Weiguo surname: Jiang fullname: Jiang, Weiguo organization: State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100101, China – sequence: 3 givenname: Ziyan surname: Ling fullname: Ling, Ziyan organization: School of Geography and Planning, Nanning Normal University, Nanning 530001, China – sequence: 4 givenname: Lulu surname: Liu fullname: Liu, Lulu organization: School of Architecture and Civil Engineering, Chengdu University, Chengdu 610106, China – sequence: 5 givenname: Shujuan orcidid: 0009-0009-4022-976X surname: Sun fullname: Sun, Shujuan organization: School of Architecture and Civil Engineering, Chengdu University, Chengdu 610106, China |
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| Keywords | Cooling effects SHAP Interaction effects Urban wetland park Random Forest |
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| Title | Revealing the driving factors of urban wetland park cooling effects using Random Forest regression and SHAP algorithm |
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