Evaluation of landslide susceptibility mapping by evidential belief function, logistic regression and support vector machine models
作者: Hyun-Joo OhPrima Riza KadaviChang-Wook LeeSaro Lee
作者单位: 1Korea Institute of Geoscience and Mineral Resources , Deajeon , Republic of Korea
2;Division of Science Education, Kangwon National University , Chuncheon , Gangwon-do , Republic of Korea
刊名: Geomatics, Natural Hazards and Risk, 2018, Vol.9 (1), pp.1053-1070
来源数据库: Taylor & Francis Journal
DOI: 10.1080/19475705.2018.1481147
关键词: Area under the curveEvidential belief function modelLandslide susceptibilityLogistic regressionSupport vector machine model
原始语种摘要: Abstract(#br)The main purpose of this study was to produce landslide susceptibility maps using evidential belief function (EBF), logistic regression (LR) and support vector machine (SVM) models and to compare their results for the region surrounding Yongin, South Korea. We compiled a landslide inventory map of 82 landslides based on reports and aerial photographs and confirmed these data through extensive field surveys. All landslides were randomly separated into two data sets of 41 landslide data points each; half were selected to establish the model, and the remaining half were used for validation. We divided 18 landslide conditioning factors into the following four categories: topography factors, hydrology factors, soil map and forest map; these were considered for landslide...
全文获取路径: Taylor & Francis  (合作)
影响因子:0.977 (2012)

  • landslide 山崩滑坡
  • susceptibility 磁化率
  • mapping 映象
  • regression 海退
  • machine 机器
  • function 函数
  • support 支柱
  • prediction 预报
  • validation 证实
  • accuracy 准确度