中国地质灾害与防治学报2025，Vol.36Issue(3)：160-170,11.DOI:10.16031/j.cnki.issn.1003-8035.202311017
RF-BP神经网络耦合模型在城市地面塌陷易发性评价中的应用
Assessment of urban ground collapse susceptibility based on RF-BP neural network coupling model:A case study of typical areas in Hangzhou City
摘要
Abstract
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 characterized by fill and silty soil.The selection of ground collapse indicators was conducted,followed by a correlation test.7 evaluation factors,including drainage pipeline density,social activity density,depth of underground confined water level,thickness of surface fill layer,distance from hidden rivers and beaches,depth of the saturated sand top plate,and thickness of the soft soil layer,were selected for assessing the susceptibility to ground subsidence in the study area.By comparing the random forest(RF)model,RF-I integrated model,and RF-BP neural network integrated model,it was found that the integrated model had higher accuracy in assessing the susceptibility of ground collapses subsidence in this study area compared to single models.Ultimately,the RF-BP neural network integrated model,which showed the best performance,was chosen for susceptibility assessment.The assessment results indicated a high correlation between the susceptibility zones and areas prone to ground subsidence,indicating good prediction performance and proving the potential application of data-driven models in evaluating the susceptibility of urban ground collapses.关键词
地面塌陷灾害/易发性评价/机器学习模型/评价因子选取Key words
ground collapse disaster/susceptibility assessment/machine learning model/selection of evaluation factors分类
天文与地球科学引用本文复制引用
于博帆,邢怀学,周丽玲,严嘉兴,张锦瑞,徐美君..RF-BP神经网络耦合模型在城市地面塌陷易发性评价中的应用[J].中国地质灾害与防治学报,2025,36(3):160-170,11.基金项目
自然资源部滨海城市地下空间地质安全重点实验室开放基金项目(BHKF2022Z02) （BHKF2022Z02）
