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[1]HAN Ling,ZHANG Tingyu,ZHANG Heng.Landslide Susceptibility Mapping Based on IOE and SVM Model in Fugu Town[J].Research of Soil and Water Conservation,2019,26(03):367-372.

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Landslide Susceptibility Mapping Based on IOE and SVM Model in Fugu Town

Research of Soil and Water Conservation[ISSN 1005-3409/CN 61-1272/P] Volume: 26 Number of periods: 2019 03 Page number: 367-372 Column: Public date: 2019-04-12

Title:: Landslide Susceptibility Mapping Based on IOE and SVM Model in Fugu Town

Author(s):: HAN Ling, ZHANG Tingyu, ZHANG Heng; Chang’an University, School of Earth Science and Resources, Key Laboratory of Degraded and Unutilized Land Remediation Engineering, Ministry of Land and Resources, Shaanxi Provincial Key Laboratory of Land Rehabilitation, Xi’an 710064, China

Keywords:: geology; susceptibility mapping; index of entropy; support vector machine; landslide

CLC:: P642.2;P208

DOI:: -

Abstract:: Fugu Town, Fugu County, Shannxi Province, was taken as the reasearch area. Through field investigation, 47 landslides have been mapped in landslide inventory map. Based on GIS software and statistical analysis model, study of landslide susceptibility mapping was carried out. Then, slope aspect, slope angle, altitude, distance to fault, distance to road, distance to river, lithology, land use, NDVI and rainfall were selected as conditioning factors to extract the factor layer. The landslide susceptibility index was calculated using the index of entropy model (IOE) and the support vector machine model (SVM), respectively. The natural break method was used to divide the study area into low, moderate, high, and very high region. Finally, the area under the ROC sensitivity curve (AUC) was used to test the partition results obtained by these two models. The results show that the AUC values of success rate and prediction rate are between 0.70 and 0.90, indicating that the two landslide susceptibility maps have high accuracy and can provide reference for landslide control in study area. The AUC values of SVM model are the highest in the training and validating samples, which means that the SVM model is suitable for landslide prediction research in the study area than IOE model.

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