An analysis of urban land subsidence susceptibility based on complex network

Yiyue Wang (), Runyu Fan (), Jining Yan (), Min Jin (), Xinya Lei (), Yuewei Wang () and Weijing Song ()
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Yiyue Wang: International Research Center of Big Data for Sustainable Development Goals
Runyu Fan: China University of Geosciences
Jining Yan: China University of Geosciences
Min Jin: China University of Geosciences
Xinya Lei: China University of Geosciences
Yuewei Wang: China University of Geosciences
Weijing Song: International Research Center of Big Data for Sustainable Development Goals

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2025, vol. 121, issue 1, No 32, 815-837

Abstract: Abstract The damage wrought by urban land subsidence increases as cities grow, engineering projects expand, and there is more human activity every year. This paper uses the complex networks method with graph neural network learning and regression models to investigate the spatial characteristics and influencing factors of land subsidence during the second phase of the construction of Shenzhen Metro Line 5 in 2019. The outcomes of the experiment reveal that (1) community testing of the settlement network produced nine settlement funnel ranges, and the largest central depth was $$-$$ - 24.52 mm/a; and (2) the graph attention network neural network divides the sensitivity of network nodes into three categories based on 15 different influencing factors. Comparison of the land subsidence funnels results reveals a considerable association between the occurrence of settlement during subway construction and both land vegetation and geological types. The study’s findings offer some scientific support for preventive and control measures for land subsidence management in urban design.

Keywords: Land subsidence susceptibility; Complex network; Community detection; Graph neural network (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11069-024-06815-7

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