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

作　　者：伊尧国[1,2,3] 刘慧平[1,2] 张洋华 刘湘平[1,2] 齐建超[1,2] 

机构地区：[1]北京师范大学地理学与遥感科学学院,北京100875 [2]遥感科学国家重点实验室,北京100875 [3]天津城建大学地质与测绘学院,天津300384

出　　处：《灾害学》2017年第1期50-59,共10页Journal of Catastrophology

基　　金：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(14JCTPJC00514)

摘　　要：以天津市东南部沉降区为例,结合权重因子模型和深度学习的方法对城市建筑群荷载作用下的地面沉降危险性进行了分析和研究。基于权重因子模型分析了研究区内建筑容积率、建筑结构和基础形式、地形坡度变化、土壤压缩模量、地下水的埋深和地下水渗透性七个方面的诱发因子对沉降危险性的影响大小,再根据WOE-DBM模型绘制了地面沉降与其诱发因子的危险性指数图。通过ROC检验表明,基于WOE-DBM模型生成的沉降危险性指数对研究区内已发生的沉降具有较好的"诊断"作用,AUC值达到了0.83,预测结果与实测结果也具有很好的一致性,从而证明该方法对于建筑物荷载引发沉降的评价和预测是非常有效的,其分析结果可以广泛应用于城市密集建筑区的地面沉降危害性预警、建筑形式选择以及城市规划的分析决策当中。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 subsidence hazard induced by building load assessment and prediction. The results can be widely used for hazard prevention, 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 

分 类 号：X43[环境科学与工程—灾害防治] P642.2[天文地球—工程地质学]