摘要
[目的]探索准确、快速的滑坡易发性区划方法,为区域安全监测提供参考,为政府治理滑坡灾害提供科学依据。[方法]以安徽省池州市贵池区为研究区域,采用梯度提升决策树—逻辑回归(GBDT-LR)和信息量(I)模型耦合的方法,实现区域滑坡易发性评价。该方法通过对原样本地学习,组合产生新的模拟样本,从而增强易发性评价模型对滑坡的拟合能力;采用Borderline-Smote算法解决样本数据不对称的问题。选用r.slopeunits软件划分的斜坡单元作为最小评价单元,选取坡度、坡向、地形曲率、剖面曲率、平面曲率、地形湿度指数(TWI)、地形起伏度、归一化植被指数(NDVI)、距断裂距离和距水系距离总计10个评价因子。分别从频率比、滑坡灾害点及隐患点密度、ROC曲线3个方面对构建的滑坡易发性模型进行评价。[结果]试验结果表明:耦合模型I-GBDT-LR分别比I,LR,I-LR模型的高易发区频率比所占比例提升约10%,13%,7%,高易发区滑坡灾害点及隐患点密度分别提升约9,11,7,ROC精度提升约10%,9%,5%。[结论]从检验指标综合来看,耦合模型的精度均高于单一模型,所提出耦合模型精度又高于I-LR耦合模型,为滑坡易发性评价提供了一种有效的、新型的评价方法。
[Objective]The accurate and rapid landslide susceptibility zoning method were studied in order to provide a reference for regional safety monitoring,and provide a scientific basis for the government to control landslide disasters.[Methods]The study was conducted in the Guichi District of Chizhou City,Anhui Province.The coupled model of gradient boosting decision tree-logistic regression(GBDT-LR)and an information value(I)model was used to determine the evaluation of regional landslide susceptibility.The model learns from the original samples and combines them to generate new simulation samples in order to enhance the fitting ability of the model to evaluate landslide susceptibility.The Borderline-Smote algorithm was used to solve the problem of sample data asymmetry.The slope unit divided by r.slopeunits software was selected as the minimum evaluation unit,and a total of 10 evaluation factors were selected:slope gradient,slope aspect,terrain curvature,profile curvature,plane curvature,topographic wetness index(TWI),topographic relief,normalized difference vegetation index(NDVI),distance from fault,and distance from river.The landslide susceptibility model was evaluated from three aspects:frequency ratio,density of landslide disaster points and hidden danger points,and the receiver operating characteristic(ROC)curve.[Results]The experimental results showed that the frequency ratio of the coupled model I-GBDT-LR was 10%,13%,and 7%greater than that of the I,LR,and I-LR models,respectively.The density of landslide disaster points and hidden danger points in the high risk area increased by about 9,11,and 7,respectively,and the ROC accuracy increased by about 10%,9%,and 5%,respectively.[Conclusion]The accuracy of the coupled model was higher than that of the single model,and the accuracy of the coupled model proposed was higher than that of the I-LR coupled model,which provides an effective and new evaluation method for landslide susceptibility evaluation.
作者
董张玉
张晋
彭鹏
王燕
杨智
安森
Dong Zhangyu;Zhang Jin;Peng Peng;Wang Yan;Yang Zhi;An Sen(〔1.School of Computer Science and Information Engineering,Hefei University of Technology,Hefei,Anhui 230601,China;Anhui Province Key Laboratory of Industry Safety and Emergency Technology,Hefei,Anhui 230601,China;Intelligent Interconnected Systems Laboratory of Anhui Provincial,Hefei,Anhui 230601,China;Geological Survey Anhui Province(Anhui Institute of Geological Sciences),Hefei,Anhui 230001,China〕)
出处
《水土保持通报》
CSCD
北大核心
2023年第1期149-157,166,共10页
Bulletin of Soil and Water Conservation
基金
安徽省重点研究与开发计划项目“典型地质灾害天地一体化协同监测关键技术研究”(20200407020030)
中央高校基本科研业务费专(JZ2021HGTB0111)
安徽省自然科学资助项目(2108085MF233)。
关键词
滑坡易发性
信息量
逻辑回归
GBDT-LR
安徽省池州市
landslide susceptibility
information value
logistic regression
gradient boosting decision tree-logistic regression(GBDT-LR)
Chizhou City of Anhui Province
