Assessing landslide susceptibility using Bayesian probability-based weight of evidence model

被引:40
作者
Sujatha, Evangelin Ramani [1 ]
Kumaravel, P. [2 ]
Rajamanickam, G. Victor [3 ]
机构
[1] SASTRA Univ, Sch Civil Engn, Thanjavur, Tamil Nadu, India
[2] Indian Inst Astrophys, Kodaikkanal, Tamil Nadu, India
[3] Sairam Grp Inst, Madras, Tamil Nadu, India
关键词
Landslide; Susceptibility; Weight of evidence; Contrast; Conditional independence; ARTIFICIAL NEURAL-NETWORKS; LOGISTIC-REGRESSION; CONDITIONAL-PROBABILITY; HAZARD EVALUATION; ZONATION; TURKEY; GIS; VERIFICATION; KOYULHISAR; CATCHMENTS;
D O I
10.1007/s10064-013-0537-9
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
This study aims to demonstrate the application of a Bayesian probability-based weight of evidence model to map landslide susceptibility in the Tevankarai stream watershed, Kodaikkanal, India. Slope gradient, relief, aspect, curvature, land use, soil, lineament density, flow accumulation and proximity to roads were the landslide conditioning factors we considered in order to assess susceptibility. The weight of evidence model uses the prior probability of occurrence of a landslide event to identify areas prone to landslides based on the relative contributions of landslide conditioning factors. A pair-wise test of conditional independence was performed for the above factors, allowing the combination of conditioning factors to be analyzed. The contrast (difference of W (+) and W (-)) was used as weight for each factor's type. The best observed combination consisted of the relief, slope, curvature, land use and distance to road factors, showing an accuracy of 86.1 %, while the accuracy of the map with all factors was 83.9 %.
引用
收藏
页码:147 / 161
页数:15
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