• DocumentCode
    2640792
  • Title

    Validation of Logistic Regression Models for Landslide Susceptibility Maps

  • Author

    Bai, S.B. ; Wang, J. ; Pozdnoukhov, A. ; Kanevski, M.

  • Author_Institution
    Nat. Educ. Adm. Key Lab. of Virtual Geographic Environments, Nanjing Normal Univ., Nanjing, China
  • Volume
    2
  • fYear
    2009
  • fDate
    March 31 2009-April 2 2009
  • Firstpage
    355
  • Lastpage
    358
  • Abstract
    A wide range of numerical models and tools have been developed over the last decades to support the decision making process in environmental applications, ranging from physical models to a variety of statistically-based methods. In this study, a landslide susceptibility map of a part of Three Gorges Reservoir region of China was produced, employing binary logistic regression analyses. The available information includes the digital elevation model of the region, geological map and different GIS layers including land cover data obtained from satellite imagery. The landslides were observed and documented during the field studies. The validation analysis is exploited to investigate the quality of mapping.
  • Keywords
    digital elevation models; geographic information systems; geomorphology; geophysics computing; statistical analysis; terrain mapping; China; Three Gorges Reservoir region; binary logistic regression analyses; decision making process; digital elevation model; environmental applications; geographic information systems; geological map; land cover data; landslide susceptibility maps; logistic regression models; numerical models; satellite imagery; statistically-based methods; Decision making; Digital elevation models; Geographic Information Systems; Geology; Logistics; Numerical models; Regression analysis; Reservoirs; Satellites; Terrain factors; Binary logistic regression; GIS; Landslide susceptibility; Validation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Science and Information Engineering, 2009 WRI World Congress on
  • Conference_Location
    Los Angeles, CA
  • Print_ISBN
    978-0-7695-3507-4
  • Type

    conf

  • DOI
    10.1109/CSIE.2009.1019
  • Filename
    5171359