灾害学2017，Vol.32Issue(1)：50-59,10.DOI:10.3969/j.issn.1000-811X.2017.01.010
融合权重因子模型和深度学习方法的城市地面沉降危险性分析
Analysis of Urban Ground Subsidence Hazard Induced by Building Load Combined with Weights of Evidence Model and Deep Learning
摘要
Abstract
Urban ground subsidence hazard induced by building load is analyzed and studied combined with weights of evidence model and deep learning method in the case of southeast subsidence areas,Tianjin,China.We discussed the value controlling or related to ground subsidence of seven major factors:building floor area ratio, structure form,basis form,slope,soil compression modulus,depth to groundwater and groundwater permeability based on weights of evidence model.we proposed the WOE-DBM model by combining the weights of evidence (WOE)with deep Boltzmann machine (DBM),which was applied to draw hazard index figure.The results were validated by receiver operating characteristic (ROC)which show the ground subsidence hazard index generated by this model has a certain "diagnostic"role on land settlement history case in the study area.The AUC is 0.83 that indicates prediction result coordinate with field survey data and certifies the model has high accuracy to ground sub-sidence hazard induced by building load assessment and prediction.The results can be widely used for hazard pre-vention,architecture pattern chosen and land-use planning in the densely urban areas.关键词
城市地面沉降/危险性分析/建筑物荷载/权重因子模型/深度学习/危险性指数Key words
urban ground subsidence/hazard analysis/building load/weights of evidence/deep learning/hazard index分类
资源环境引用本文复制引用
伊尧国,刘慧平,张洋华,刘湘平,齐建超..融合权重因子模型和深度学习方法的城市地面沉降危险性分析[J].灾害学,2017,32(1):50-59,10.基金项目
国家自然科学基金重点项目（40671127）；中央高校基本科研业务费专项资金；测绘遥感信息工程国家重点实验室开放研究基金（（12）重02）；天津市科委科技特派员项目 （）
