Examining the Relationship between Land Use/Land Cover (LULC) and Land Surface Temperature (LST) Using Explainable Artificial Intelligence (XAI) Models: A Case Study of Seoul, South Korea

Minjun Kim, Dongbeom Kim and Geunhan Kim ()
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Minjun Kim: Department of Environmental Planning, Korea Environment Institute, Sejong 30147, Republic of Korea
Dongbeom Kim: Technical Research Institute NEGGA Co., Ltd., Seoul 07220, Republic of Korea
Geunhan Kim: Department of Environmental Planning, Korea Environment Institute, Sejong 30147, Republic of Korea

IJERPH, 2022, vol. 19, issue 23, 1-16

Abstract: 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.

Keywords: land-use/land-cover (LULC); land surface temperature (LST); explainable artificial intelligence (XAI); Sharpley additive explanations (SHAP); remote sensing (RS) (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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