Application of Explainable Artificial Intelligence (XAI) in Urban Growth Modeling: A Case Study of Seoul Metropolitan Area, Korea

Minjun Kim, Dongbeom Kim, Daeyong Jin 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
Daeyong Jin: Center for Environment Data Strategy, Korea Environment Institute, Sejong 30147, Republic of Korea
Geunhan Kim: Department of Environmental Planning, Korea Environment Institute, Sejong 30147, Republic of Korea

Land, 2023, vol. 12, issue 2, 1-17

Abstract: Unplanned and rapid urban growth requires the reckless expansion of infrastructure including water, sewage, energy, and transportation facilities, and thus causes environmental problems such as deterioration of old towns, reduction of open spaces, and air pollution. To alleviate and prevent such problems induced by urban growth, the accurate prediction and management of urban expansion is crucial. In this context, this study aims at modeling and predicting urban expansion in Seoul metropolitan area (SMA), Korea, using GIS and XAI techniques. To this end, we examined the effects of land-cover, socio-economic, and environmental features in 2007 and 2019, within the optimal radius from a certain raster cell. Then, this study combined the extreme gradient boosting (XGBoost) model and Shapley additive explanations (SHAP) in analyzing urban expansion. The findings of this study suggest urban growth is dominantly affected by land-cover characteristics, followed by topographic attributes. In addition, the existence of water body and high ECVAM grades tend to significantly reduce the possibility of urban expansion. The findings of this study are expected to provide several policy implications in urban and environmental planning fields, particularly for effective and sustainable management of lands.

Keywords: urban growth model; explainable artificial intelligence (XAI); extreme gradient boosting (XGBoost); Shapley additive explanations (SHAP) (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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