Landslide susceptibility assessment at the Wuning area, China: a comparison between multi-criteria decision making, bivariate statistical and machine learning methods

被引:0
作者
Haoyuan Hong
Himan Shahabi
Ataollah Shirzadi
Wei Chen
Kamran Chapi
Baharin Bin Ahmad
Majid Shadman Roodposhti
Arastoo Yari Hesar
Yingying Tian
Dieu Tien Bui
机构
[1] Nanjing Normal University,Key Laboratory of Virtual Geographic Environment
[2] State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province),Department of Geomorphology, Faculty of Natural Resources
[3] Jiangsu Center for Collaborative Innovation in Geographic Information Resource Development and Application,Department of Rangeland and Watershed Management, Faculty of Natural Resources
[4] University of Kurdistan,College of Geology and Environment
[5] University of Kurdistan,Department of Geoinformation, Faculty of Geoinformation and Real Estate
[6] Xi’an University of Science and Technology,Discipline of Geography and Spatial Sciences, School of Land and Food
[7] Universiti Teknologi Malaysia (UTM),Department of Geography
[8] University of Tasmania,Geographic Information Science Research Group
[9] University of Mohaghegh Ardabili,Faculty of Environment and Labour Safety
[10] Jiangxi Provincial Meteorological Observatory,undefined
[11] Jiangxi Meteorological Bureau,undefined
[12] Ton Duc Thang University,undefined
[13] Ton Duc Thang University,undefined
来源
Natural Hazards | 2019年 / 96卷
关键词
Landslide susceptibility; Natural disaster; Support vector machine; Spatial multi-criteria evaluation; Weighted linear combination;
D O I
暂无
中图分类号
学科分类号
摘要
The aim of this research is to investigate multi-criteria decision making [spatial multi-criteria evaluation (SMCE)], bivariate statistical methods [frequency ratio (FR), index of entropy (IOE), weighted linear combination (WLC)] and machine learning [support vector machine (SVM)] models for estimating landslide susceptibility at the Wuning area, China. A total of 445 landslides were randomly classified into 70% (311 landslides) and 30% (134 landslides) to train and validate landslide models, respectively. Fourteen landslide conditioning factors including slope angle, slope aspect, altitude, topographic wetness index, stream power index, sediment transport index, soil, lithology, NDVI, land use, rainfall, distance to road, distance to river and distance to fault were then studied for landslide susceptibility assessment. Performances of five studied models were evaluated using area under the ROC curve (AUROC) for training (success rate curve) and validation (prediction rate curve) datasets, statistical-based measures and tests. Results indicated that the area under the success rate curve for the FR, IOE, WLC, SVM and SMCE models was 88.32%, 82.58%, 78.91%, 85.47% and 89.96%, respectively, demonstrating that SMCE could provide the higher accuracy. The prediction capability findings revealed that the SMCE model (AUC = 86.81%) was also the highest approach among the five studied models, followed by the FR (AUC = 84.53%), the SVM (AUC = 81.24%), the IOE (AUC = 79.67%) and WLC (73.92%) methods. The landslide susceptibility maps derived from the above five models are reasonably accurate and could be used to perform elementary land use planning for hazard extenuation.
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页码:173 / 212
页数:39
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