Department of Environmental Planning, Korea Environment Institute, Sejong 30147, Republic of Korea.
Technical Research Institute NEGGA Co., Ltd., Seoul 07220, Republic of Korea.
Int J Environ Res Public Health. 2022 Nov 29;19(23):15926. doi: 10.3390/ijerph192315926.
Understanding the relationship between land use/land cover (LULC) and land surface temperature (LST) has long been an area of interest in urban and environmental study fields. To examine this, existing studies have utilized both white-box and black-box approaches, including regression, decision tree, and artificial intelligence models. To overcome the limitations of previous models, this study adopted the explainable artificial intelligence (XAI) approach in examining the relationships between LULC and LST. By integrating the XGBoost and SHAP model, we developed the LST prediction model in Seoul and estimated the LST reduction effects after specific LULC changes. Results showed that the prediction accuracy of LST was maximized when landscape, topographic, and LULC features within a 150 m buffer radius were adopted as independent variables. Specifically, the existence of surrounding built-up and vegetation areas were found to be the most influencing factors in explaining LST. In this study, after the LULC changes from expressway to green areas, approximately 1.5 °C of decreasing LST was predicted. The findings of our study can be utilized for assessing and monitoring the thermal environmental impact of urban planning and projects. Also, this study can contribute to determining the priorities of different policy measures for improving the thermal environment.
理解土地利用/土地覆盖（LULC）和地表温度（LST）之间的关系一直是城市和环境研究领域的研究重点。为了研究这一点，现有研究已经使用了白盒和黑盒方法，包括回归、决策树和人工智能模型。为了克服以前模型的局限性，本研究采用了可解释人工智能（XAI）方法来研究 LULC 和 LST 之间的关系。通过整合 XGBoost 和 SHAP 模型，我们在首尔开发了 LST 预测模型，并估计了特定 LULC 变化后的 LST 降低效果。结果表明，当将景观、地形和 150m 缓冲区半径内的 LULC 特征作为自变量时，LST 的预测精度达到最大。具体来说，发现周围建成区和植被区的存在是解释 LST 的最主要影响因素。在本研究中，预测从高速公路变为绿地后，LST 将降低约 1.5°C。本研究的结果可用于评估和监测城市规划和项目对热环境的影响。此外，本研究还可以为确定改善热环境的不同政策措施的优先次序提供参考。
