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  2. 地质与地球物理研究所
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  4. 中国科学院页岩气与地质工程重点实验室
An integrated approach for landslide susceptibility mapping by considering spatial correlation and fractal distribution of clustered landslide data

文献类型：期刊论文

作者Liu, Linan1,2,3; Li, Shouding1,2,3; Li, Xiao1,2,3; Jiang, Yue4; Wei, Wenhui4; Wang, Zhanhe4; Bai, Yaheng5
刊名LANDSLIDES
出版日期2019-04-01
卷号16期号:4页码:715-728
ISSN号1612-510X
关键词Landslide clustering Fractal Spatial statistics Validation statistics Landslide susceptibility mapping
DOI10.1007/s10346-018-01122-2
英文摘要Natural disasters often show highly heterogeneous character due to complex geo-environmental settings. The spatial distribution of landslides is generally clustered at different scales. In this paper, we proposed a methodology for landslide susceptibility mapping (LSM) with consideration of spatial correlation and distribution of clustered landslide data. To quantify the spatial correlation of landslides, a normalized spatial-correlated scale index (NSCI) was introduced. Based on the definition of landslide frequency ratio, calibrated landslide potential index (CLPI) was proposed to account for the effect of landslide clustering. Considering the fractal distribution of landslides, the variable fractal dimension model (VFDM) was introduced to measure the spatial association between clustered landslides and conditional factors. Based on the definition of fractal dimension (D), the weights of the factors were obtained from fractal perspective. We proposed a weighted calibrated landslide potential model (WCLPM), obtained by the combination of CLPI values and weights of the factors. The proposed method is illustrated by example in Xinjiang, NW China, where landslide points are clustered at regional scale. In the example, the landslides were randomly split into two groups: one for building landslide model (training dataset) and the other for validating the model (validating dataset). Five landslide conditional factors (lithology, tectonic faults, elevation, slope, aspect) were selected, processed, and analyzed in a geographic information system (GIS) environment. Predictive accuracy of the WCLPM was evaluated and compared based on the calculation of area under the prediction-rate curve (AUPRC). The example shows that the proposed WCLPM provides good prediction for the study area (AUPRC=0.8700). This study provided a novel and practical method for LSM.
WOS关键词LOGISTIC-REGRESSION ; HAZARD ; GIS ; RESOLUTION ; MODEL ; INFORMATION ; ACCURACY ; PROVINCE ; WEIGHTS ; ISLAND
资助项目National Natural Science Foundation of China[41272352] ; National Key Research and Development Program of China[2018YFC1504803-01] ; Science and Technology Project of Xinjiang Land and Resource Department[XJDZFZ-XX2013]
WOS研究方向Engineering ; Geology
语种英语
出版者SPRINGER HEIDELBERG
WOS记录号WOS:000463099000004
资助机构National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; Science and Technology Project of Xinjiang Land and Resource Department ; Science and Technology Project of Xinjiang Land and Resource Department ; Science and Technology Project of Xinjiang Land and Resource Department ; Science and Technology Project of Xinjiang Land and Resource Department ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; Science and Technology Project of Xinjiang Land and Resource Department ; Science and Technology Project of Xinjiang Land and Resource Department ; Science and Technology Project of Xinjiang Land and Resource Department ; Science and Technology Project of Xinjiang Land and Resource Department ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; Science and Technology Project of Xinjiang Land and Resource Department ; Science and Technology Project of Xinjiang Land and Resource Department ; Science and Technology Project of Xinjiang Land and Resource Department ; Science and Technology Project of Xinjiang Land and Resource Department ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; Science and Technology Project of Xinjiang Land and Resource Department ; Science and Technology Project of Xinjiang Land and Resource Department ; Science and Technology Project of Xinjiang Land and Resource Department ; Science and Technology Project of Xinjiang Land and Resource Department
源URL[http://ir.iggcas.ac.cn/handle/132A11/91284]  
专题地质与地球物理研究所_中国科学院页岩气与地质工程重点实验室
通讯作者Li, Shouding
作者单位1.Chinese Acad Sci, Inst Geol & Geophys, Key Lab Shale Gas & Geoengn, Beijing 100029, Peoples R China
2.Chinese Acad Sci, Inst Earth Sci, Beijing 100029, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100046, Peoples R China
4.Xinjiang Inst Geol Environm Monitoring, Urumqi 830002, Peoples R China
5.Henan Prov Commun Planning & Design Inst Co Ltd, Zhengzhou 450052, Henan, Peoples R China
推荐引用方式
GB/T 7714		 Liu, Linan,Li, Shouding,Li, Xiao,et al. An integrated approach for landslide susceptibility mapping by considering spatial correlation and fractal distribution of clustered landslide data[J]. LANDSLIDES,2019,16(4):715-728.
APA Liu, Linan.,Li, Shouding.,Li, Xiao.,Jiang, Yue.,Wei, Wenhui.,...&Bai, Yaheng.(2019).An integrated approach for landslide susceptibility mapping by considering spatial correlation and fractal distribution of clustered landslide data.LANDSLIDES,16(4),715-728.
MLA Liu, Linan,et al."An integrated approach for landslide susceptibility mapping by considering spatial correlation and fractal distribution of clustered landslide data".LANDSLIDES 16.4(2019):715-728.

入库方式： OAI收割

来源：地质与地球物理研究所

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