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Small-scale, large impact: utilizing machine learning to assess susceptibility to urban geological disasters—a case study of urban road collapses in Hangzhou

Crossref DOI link: https://doi.org/10.1007/s10064-024-03931-3

Published Online: 2024-10-23

Published Print: 2024-11

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Yu, Bofan

Xing, Huaixue https://orcid.org/0009-0006-8423-6011
Yan, Jiaxing

Li, Yunan
Funding

Funding for this research was provided by:

Laboratory of Geological Safety of Underground Space in Coastal Cities, Ministry of Natural Resources (BHKF2022Z02)
License Information

Text and Data Mining valid from 2024-10-23

Version of Record valid from 2024-10-23
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Article History

Received: 18 May 2024

Accepted: 3 September 2024

First Online: 23 October 2024

Declarations

:

: The authors declare no competing interests.

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