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A Study on Identification of Urban Waterlogging Risk Factors Based on Satellite Image Semantic Segmentation and XGBoost

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  • Jinping Tong

    (Business School, Changzhou University, Changzhou 213100, China
    Management Science Institute, Hohai University, Nanjing 210098, China)

  • Fei Gao

    (Business School, Changzhou University, Changzhou 213100, China)

  • Hui Liu

    (Business School, Changzhou University, Changzhou 213100, China)

  • Jing Huang

    (Management Science Institute, Hohai University, Nanjing 210098, China
    Business School, Hohai University, Nanjing 210098, China)

  • Gaofeng Liu

    (Business School, Hohai University, Nanjing 210098, China)

  • Hanyue Zhang

    (Business School, Changzhou University, Changzhou 213100, China)

  • Qiong Duan

    (Information Technology Center, Luoyang Institute of Science and Technology, Luoyang 471023, China)

Abstract

As global warming exacerbates and urbanization accelerates, extreme climatic events occur frequently. Urban waterlogging is seriously spreading in China, resulting in a high level of vulnerability in urban societies and economies. It has been urgent for regional sustainable development to effectively identify and analyze the risk factors behind urban waterlogging. A novel model incorporating satellite image semantic segmentation into extreme gradient boosting (XGBoost) is employed for identifying and forecasting the urban waterlogging risk factors. Ground object features of waterlogging points are extracted by the satellite image semantic segmentation, and XGBoost is employed to predict waterlogging points and identify the primary factors affecting urban waterlogging. This paper selects the coastal cities of Haikou, Xiamen, Shanghai, and Qingdao as research areas, and obtains data from social media. According to the comprehensive performance evaluation of the semantic segmentation and XGBoost models, the semantic segmentation model could effectively identify and extract water bodies, roads, and green spaces in satellite images, and the XGBoost model is more accurate and reliable than other common machine learning methods in prediction performance and precision. Among all waterlogging risk factors, elevation is the main factor affecting waterlogging in the research areas. For Shanghai and Qingdao, the secondary factor affecting waterlogging is roads. Water bodies are the secondary factor affecting urban waterlogging in Haikou. For Xiamen, the four indicators other than the elevation are equally significant, which could all be regarded as secondary factors affecting urban waterlogging.

Suggested Citation

  • Jinping Tong & Fei Gao & Hui Liu & Jing Huang & Gaofeng Liu & Hanyue Zhang & Qiong Duan, 2023. "A Study on Identification of Urban Waterlogging Risk Factors Based on Satellite Image Semantic Segmentation and XGBoost," Sustainability, MDPI, vol. 15(8), pages 1-15, April.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:8:p:6434-:d:1119991
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    References listed on IDEAS

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    1. Huafei Yu & Yaolong Zhao & Yingchun Fu & Le Li, 2018. "Spatiotemporal Variance Assessment of Urban Rainstorm Waterlogging Affected by Impervious Surface Expansion: A Case Study of Guangzhou, China," Sustainability, MDPI, vol. 10(10), pages 1-22, October.
    2. Lin Lin & Zening Wu & Qiuhua Liang, 2019. "Urban flood susceptibility analysis using a GIS-based multi-criteria analysis framework," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 97(2), pages 455-475, June.
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    Cited by:

    1. Juan Huang & Jiangfeng Li & Zhi Huang, 2023. "Identification of Waterlogging-Prone Areas in Nanning from the Perspective of Urban Expansion," Sustainability, MDPI, vol. 15(20), pages 1-17, October.

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