文献类型：期刊文献

英文题名：Exploring the non-linear impacts of urban features on land surface temperature using explainable artificial intelligence

作者：Feng, Fei[1] Ren, Yaxue[2] Xu, Chengyang[1] Jia, Baoquan[3] Wu, Shengbiao[4] Lafortezza, Raffaele[1,2]

第一作者：Feng, Fei

通信作者：Lafortezza, R[1]

机构：[1]Beijing Forestry Univ, Res Ctr Urban Forestry, Key Lab Silviculture & Forest Ecosyst, State Forestry & Grassland Adm, Beijing 100083, Peoples R China;[2]Univ Bari Aldo Moro, Dept Soil Plant & Food Sci, Via Amendola 165-A, I-70126 Bari, Italy;[3]Chinese Acad Forestry, Res Inst Forestry, Beijing 100091, Peoples R China;[4]Univ Hong Kong, Fac Architecture, Dept Architecture, Div Landscape Architecture,Future Urban & Sustaina, Hong Kong, Peoples R China

年份：2024

卷号：56

外文期刊名：URBAN CLIMATE

收录：;Scopus(收录号：2-s2.0-85196810486);WOS:【SCI-EXPANDED(收录号:WOS:001261954400001)】；

基金：This study was funded by the Fundamental Research Funds for the Central Universities (BLX 202201) and carried out under the research project "CLEARING HOUSE - Collaborative Learning in Research, Information-sharing and Governance on How Urban tree-based solutions support Sino-European urban futures", funded by the European Union's Horizon 2020 Research and Innovation Program (Grant Agreement No. 821242) . We would like to thank Yole DeBellis for revising the manuscript.r based solutions support Sino-European urban futures", funded by the European Union's Horizon 2020 Research and Innovation Program (Grant Agreement No. 821242) . We would like to thank Yole DeBellis for revising the manuscript.

语种：英文

外文关键词：Shapley additive explanations; Urbanization impact; Land surface temperature; Building structure; Urban vegetation; Urban climate research

摘要：High land surface temperatures (LST) have emerged as crucial threats to urban ecosystems and sustainable urban development. To better understand and mitigate their impacts, it is essential to analyze the contributing urban features. Against this background, we developed a random forest model enhanced by Explainable Artificial Intelligence (XAI) to analyze the impact features of LST in Beijing, China. By applying the XAI method, our results suggest that the major impact features of LST in Beijing are elevation (44.19%), compactness of impervious surface (17.27%), Normalized Difference Vegetation Index (11.12%), proportion of impervious surface area (8.04%), and tree height (3.83%). Compactness of impervious surface exhibited an overall cooling effect, which became weaker at high values. LST increased with building height, and the trend became weaker as building height reached 5 m. The most important features impacting LST in the inner city are the proportion and height of buildings, whereas in the outer city these features are tree height and the compactness of impervious surfaces. The study applies XAI to explain the non-linear interactions between LST and urban features, offering innovative insights to policy-makers to develop sustainable urban planning strategies. Our findings suggest that increasing green spaces and water bodies as well as controlling building density and height can effectively mitigate heat in dense urban areas and enhance cooling effects.