Landslide susceptibility mapping based on GIS and information value model for the Chencang District of Baoji, China

被引:84
作者
Chen, Wei [1 ]
Li, Wenping [1 ]
Hou, Enke [2 ]
Zhao, Zhou [2 ]
Deng, Niandong [2 ]
Bai, Hanying [1 ]
Wang, Danzhi [1 ]
机构
[1] China Univ Min & Technol, Sch Resources & Earth Sci, Xuzhou 221116, Peoples R China
[2] Xian Univ Sci & Technol, Sch Geol & Environm, Xian 710054, Peoples R China
基金
美国国家科学基金会;
关键词
GIS; Landslide; Susceptibility mapping; Information value model; Baoji; China; SUPPORT VECTOR MACHINE; ANALYTICAL HIERARCHY PROCESS; ARTIFICIAL NEURAL-NETWORKS; HOA BINH PROVINCE; LOGISTIC-REGRESSION; FREQUENCY RATIO; CONDITIONAL-PROBABILITY; SAMPLING STRATEGIES; DECISION-TREE; MOUNTAINS;
D O I
10.1007/s12517-014-1369-z
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
The main objective of this study was to apply a statistical (information value) model using geographic information system (GIS) to the Chencang District of Baoji, China. Landslide locations within the study area were identified using reports and aerial photographs, and a field survey. A total of 120 landslides were mapped, of which 84 (70 %) were randomly selected for building the landslide susceptibility model. The remaining 36 (30 %) were used for model validation. We considered a total of 10 potential factors that predispose an area to a landslide for the landslide susceptibility mapping. These included slope degree, altitude, slope aspect, plan curvature, geomorphology, distance from faults, lithology, land use, mean annual rainfall, and peak ground acceleration. Following an analysis of these factors, a landslide susceptibility map was produced using the information value model with GIS. The resulting landslide susceptibility index was divided into five classes (very high, high, moderate, low, and very low) using the natural breaks method. The corresponding distribution area percentages were 29.22, 25.14, 15.66, 15.60, and 14.38 %, respectively. Finally, landslide locations were used to validate the results of the landslide susceptibility map using areas under the curve (AUC). The AUC plot showed that the susceptibility map had a success rate of 81.79 % and a prediction accuracy of 82.95 %. Based on the results of the AUC evaluation, the landslide susceptibility map produced using the information value model exhibited good performance.
引用
收藏
页码:4499 / 4511
页数:13
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