Application of frequency ratio, weights of evidence and evidential belief function models in landslide susceptibility mapping

被引:181
作者
Ding, Qingfeng [1 ]
Chen, Wei [2 ]
Hong, Haoyuan [3 ]
机构
[1] Zhejiang Commun Serv Co Ltd Consulting Branch, Hangzhou, Zhejiang, Peoples R China
[2] Xian Univ Sci & Technol, Sch Geol & Environm, Xian, Peoples R China
[3] Jiangxi Meteorol Bur, Jiangxi Prov Meteorol Observ, Nanchang, Jiangxi, Peoples R China
关键词
Landslide; susceptibility mapping; frequency ratio (FR); weights of evidence (WOE); evidential belief function (EBF); ANALYTICAL HIERARCHY PROCESS; SUPPORT VECTOR MACHINE; ARTIFICIAL NEURAL-NETWORKS; YIHUANG AREA CHINA; HOA BINH PROVINCE; OF-EVIDENCE MODEL; LOGISTIC-REGRESSION; SPATIAL PREDICTION; CERTAINTY FACTOR; DEMPSTER-SHAFER;
D O I
10.1080/10106049.2016.1165294
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The landslide hazard occurred in Taibai County has the characteristics of the typical landslides in mountain hinterland. The slopes mainly consist of residual sediments and locate along the highway. Most of them are in the less stable state and in high risk during rainfall in flood season especially. The main purpose of this paper is to produce landslide susceptibility maps for Taibai County (China). In the first stage, a landslide inventory map and the input layers of the landslide conditioning factors were prepared in the geographic information system supported by field investigations and remote sensing data. The landslides conditioning factors considered for the study area were slope angle, altitude, slope aspect, plan curvature, profile curvature, distance to faults, distance to rivers, distance to roads, normalized difference vegetation index, lithological unit, rainfall and land use. Subsequently, the thematic data layers of conditioning factors were integrated by frequency ratio (FR), weights of evidence (WOE) and evidential belief function (EBF) models. As a result, landslide susceptibility maps were obtained. In order to compare the predictive ability of these three models, a validation procedure was conducted. The curves of cumulative area percentage of ordered index values vs. the cumulative percentage of landslide numbers were plotted and the values of area under the curve (AUC) were calculated. The predictive ability was characterized by the AUC values and it indicates that all these models considered have relatively similar and high accuracies. The success rate of FR, WOE and EBF models was 0.9161, 0.9132 and 0.9129, while the prediction rate of the three models was 0.9061, 0.9052 and 0.9007, respectively. Considering the accuracy and simplicity comprehensively, the FR model is the optimum method. These landslide susceptibility maps can be used for preliminary land use planning and hazard mitigation purpose.
引用
收藏
页码:619 / 639
页数:21
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