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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Minjun Kim & Dongbeom Kim & Daeyong Jin & Geunhan Kim, 2023. "Application of Explainable Artificial Intelligence (XAI) in Urban Growth Modeling: A Case Study of Seoul Metropolitan Area, Korea," Land, MDPI, vol. 12(2), pages 1-17, February.
    • Handle: RePEc:gam:jlands:v:12:y:2023:i:2:p:420-:d:1058936
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    References listed on IDEAS

    as
    1. Minjun Kim & Dongbeom Kim & Geunhan Kim, 2022. "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," IJERPH, MDPI, vol. 19(23), pages 1-16, November.
    2. Soyoung Kim & Yae-Jean Kim & Kyong Ran Peck & Youngsuk Ko & Jonggul Lee & Eunok Jung, 2020. "Keeping Low Reproductive Number Despite the Rebound Population Mobility in Korea, a Country Never under Lockdown during the COVID-19 Pandemic," IJERPH, MDPI, vol. 17(24), pages 1-9, December.
    3. Hyun-Jung Hong & Choong-Ki Kim & Hyun-Woo Lee & Woo-Kyun Lee, 2021. "Conservation, Restoration, and Sustainable Use of Biodiversity Based on Habitat Quality Monitoring: A Case Study on Jeju Island, South Korea (1989–2019)," Land, MDPI, vol. 10(8), pages 1-15, July.
    4. Yingjie Hu & Xiangbin Kong & Ji Zheng & Jin Sun & Linlin Wang & Mingzhe Min, 2018. "Urban Expansion and Farmland Loss in Beijing during 1980–2015," Sustainability, MDPI, vol. 10(11), pages 1-20, October.
    5. Choi, Chang Gyu & Lee, Sugie & Kim, Heungsoon & Seong, Eun Yeong, 2019. "Critical junctures and path dependence in urban planning and housing policy: A review of greenbelts and New Towns in Korea’s Seoul metropolitan area," Land Use Policy, Elsevier, vol. 80(C), pages 195-204.
    6. Dadashpoor, Hashem & Ahani, Somayeh, 2021. "Explaining objective forces, driving forces, and causal mechanisms affecting the formation and expansion of the peri-urban areas: A critical realism approach," Land Use Policy, Elsevier, vol. 102(C).
    7. Rifat, Shaikh Abdullah Al & Liu, Weibo, 2022. "Predicting future urban growth scenarios and potential urban flood exposure using Artificial Neural Network-Markov Chain model in Miami Metropolitan Area," Land Use Policy, Elsevier, vol. 114(C).
    8. Jiabo Xu & Xingping Wang, 2020. "Reversing Uncontrolled and Unprofitable Urban Expansion in Africa through Special Economic Zones: An Evaluation of Ethiopian and Zambian Cases," Sustainability, MDPI, vol. 12(21), pages 1-20, November.
    9. Bernard Fosu Frimpong & Frank Molkenthin, 2021. "Tracking Urban Expansion Using Random Forests for the Classification of Landsat Imagery (1986–2015) and Predicting Urban/Built-Up Areas for 2025: A Study of the Kumasi Metropolis, Ghana," Land, MDPI, vol. 10(1), pages 1-21, January.
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