A hybrid fuzzy weight of evidence method in landslide susceptibility analysis on the Wuyuan area, China

被引:119
作者
Hong, Haoyuan [1 ,2 ]
Ilia, Ioanna [1 ,3 ]
Tsangaratos, Paraskevas [1 ,3 ]
Chen, Wei [4 ]
Xu, Chong [1 ]
机构
[1] China Earthquake Adm, Inst Geol, Key Lab Act Tecton & Volcano, 1 Huayanli,POB 9803, Beijing 100029, Peoples R China
[2] Nanjing Noma Univ, Coll Geog Sci, Nanjing 210023, Jiangsu, Peoples R China
[3] Natl Tech Univ Athens, Dept Geol Studies, Sch Min & Met Engn, Heroon Polytech 9, Zografos 15780, Greece
[4] Xian Univ Sci & Technol, Coll Geol & Environm, Xian 710054, Peoples R China
基金
中国国家自然科学基金;
关键词
Landslide susceptibility; Fuzzy logic; Weight of evidence; China; ARTIFICIAL NEURAL-NETWORKS; ANALYTICAL HIERARCHY PROCESS; SUPPORT VECTOR MACHINE; LOGISTIC-REGRESSION; CERTAINTY FACTOR; FREQUENCY RATIO; NATURAL SLOPES; GIS; HAZARD; PREDICTION;
D O I
10.1016/j.geomorph.2017.04.002
中图分类号
P9 [自然地理学];
学科分类号
0705 ; 070501 ;
摘要
The present study proposed a hybrid fuzzy weight of evidence model for constructing a landslide susceptibility map in the Wuyuan area, China, where disastrous landslide events have occurred. The model combines the knowledge of experts obtained through a fuzzy logic approach and a hybrid weight of evidence method. The estimated knowledge-based fuzzy membership value of each environmental variable is combined with data based conditional probabilities to derive fuzzy posterior probabilities and landslide susceptibility. The developed model was compared with a landslide susceptibility map produced using the data-driven weight of evidence method, based on 510 landslide and non-landslide sites. The sites were identified by analyzing airborne imagery, field investigation and previous studies. Landside susceptibility for these sites was analyzed using 10 geoenvironmental variables: slope, aspect, lithology, soil, rainfall, plan curvature, the normalized difference vegetation index, distance to roads, distance to rivers and distance to faults. The resultant hybrid fuzzy weight of evidence method showed high predictive power, with the area under the success and predictive curves being 0.770 and 0.746, respectively. Additional analyses showed that the developed model could work effectively even with limited data.
引用
收藏
页码:1 / 16
页数:16
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