, Volume 6, Issue 1, pp 17–26 | Cite as

Landslide susceptibility zonation mapping and its validation in part of Garhwal Lesser Himalaya, India, using binary logistic regression analysis and receiver operating characteristic curve method

  • John MathewEmail author
  • V. K. Jha
  • G. S. Rawat
Original Article


A landslide susceptibility zonation (LSZ) map helps to understand the spatial distribution of slope failure probability in an area and hence it is useful for effective landslide hazard mitigation measures. Such maps can be generated using qualitative or quantitative approaches. The present study is an attempt to utilise a multivariate statistical method called binary logistic regression (BLR) analysis for LSZ mapping in part of the Garhwal Lesser Himalaya, India, lying close to the Main Boundary Thrust (MBT). This method gives the freedom to use categorical and continuous predictor variables together in a regression analysis. Geographic Information System has been used for preparing the database on causal factors of slope instability and landslide locations as well as for carrying out the spatial modelling of landslide susceptibility. A forward stepwise logistic regression analysis using maximum likelihood estimation method has been used in the regression. The constant and the coefficients of the predictor variables retained by the regression model have been used to calculate the probability of slope failure for the entire study area. The predictive logistic regression model has been validated by receiver operating characteristic curve analysis, which has given 91.7% accuracy for the developed BLR model.


Landslide GIS Binary logistic regression 



The authors sincerely thank Dr. V. Jayaraman, Director, NNRMS/EOS, Department of Space, ISRO, India, for permitting them to carry out the study. JM thanks Dr. K. P. Sharma, Head, RRSSC, Dehradun, for the constant encouragement and motivation. Dr. N. S. Virdi, Former Director and Dr. G. Philip, Scientist, Wadia Institute of Himalayan Geology, Dehradun, extended their help and support for carrying out this study. Prof. M. L. Süzen, METU, Turkey and Prof. John C. Davis, Department of Petroleum Engineering, Leoben, Austria provided valuable suggestions on logistic regression model.


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Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  1. 1.RRSSC, ISRO, Department of SpaceIndian Space Research OrganizationDehradunIndia
  2. 2.IIRS, ISRO, Department of SpaceIndian Space Research OrganizationDehradunIndia
  3. 3.Department of GeologyHNB Garhwal UniversitySrinagarIndia

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