Assessment of urban ground collapse susceptibility based on RF-BP neural network coupling model: A case study of typical areas in Hangzhou City

To improve the current situation where ground subsidence susceptibility assessment mainly relies on knowledge-driven models, this study explores the feasibility of incorporating data-driven models into the evaluation of urban ground subsidence. The study focused on a typical area in Hangzhou charact...

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Bibliografiset tiedot

Päätekijät:	Bofan YU, Huaixue XING, Liling ZHOU, Jiaxing YAN, Jinrui ZHANG, Meijun XU
Aineistotyyppi:	Artigo
Kieli:	Chinês
Julkaistu:	Editorial Office of The Chinese Journal of Geological Hazard and Control 2025-06-01
Sarja:	Zhongguo dizhi zaihai yu fangzhi xuebao
Aiheet:	ground collapse disaster susceptibility assessment machine learning model selection of evaluation factors
Linkit:	https://www.zgdzzhyfzxb.com/en/article/doi/10.16031/j.cnki.issn.1003-8035.202311017
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