Spatial model integration for shallow landslide susceptibility and its runout using a GIS-based approach in Yongin, Korea

被引:27
|
作者
Pradhan, Ananta Man Singh [1 ]
Kang, Hyo-Sub [1 ]
Lee, Saro [2 ]
Kim, Yun-Tae [1 ]
机构
[1] Pukyong Natl Univ, Dept Ocean Engn, Geosyst Engn Lab, Busan, South Korea
[2] Korea Inst Geosci & Mineral Resources KIGAM, Div Geol Res, Daejeon, South Korea
基金
新加坡国家研究基金会;
关键词
Evidential belief function (EBF); integrated landslide susceptibility; runout propagation; EVIDENTIAL BELIEF FUNCTIONS; DEBRIS FLOWS; LOGISTIC-REGRESSION; HAZARD ASSESSMENT; ELEVATION MODELS; NEURAL-NETWORKS; NW NICARAGUA; SOIL; PREDICTION; ZONATION;
D O I
10.1080/10106049.2016.1155658
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Rainfall-triggered shallow landslide is very common in Korean mountains and the socioeconomic impact is much higher than in the past due to population pressure in hazardous zones. Present study is an attempt toward the development of a methodology for the integration of shallow landslide susceptibility zones and runout zones that could be reached by mobilized mass. Landslide occurrence areas in Yongin were determined based on the interpretation of aerial photographs and extensive field surveys. Nineteen landslide-related factors maps were collected and analysed in geographic information system environment. Among 109 identified landslides, about 85% randomly selected training landslide data from inventory map was used to generate an evidential belief function model and remaining 15% landslides were used to validate the shallow landslide susceptibility map. The resulting susceptibility map had a success rate of 89.2% and a predictive accuracy of 92.1%. A runout propagation from high susceptible area was obtained from the modified multiple-flow direction algorithm. A matrix was used to integrate the shallow landslide susceptibility classes and the runout probable zone. Thus, each pixel had a susceptibility class in relation to its failure probability and runout susceptibility class. The study of landslide potential and its propagation can be used to obtain a spatial prediction for landslides, which could contribute to landslide risk mitigation.
引用
收藏
页码:420 / 441
页数:22
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