Logistic Regression (LR) Model and Landslide Susceptibility: A RS and GIS-Based Approach

  • Sujit Mandal
  • Subrata Mondal


The application of geo-informatics has brought a new dimension in the study of landslide susceptibility assessment and prediction all over the world for regional development and planning of mountain terrain. The present study is dealt with the preparation of a landslide susceptibility map of Darjeeling Himalaya, a tectonically active section of Himalayan mountain range using logistic regression model on GIS environment. A landslide inventory map was developed in consultation with topographical maps, Google earth image, satellite images, and historical landslide events and was verified with the field data. In the study 15 landslide conditioning factors, i.e. elevation, slope aspect, slope angle, slope curvature, geology, soil, lineament density, distance to lineament, drainage density, distance to drainage, stream power index (SPI), topographic wetted index (TWI), rainfall, normalized differential vegetation index (NDVI) and land use and land cover (LULC) were taken into account and finally their integration has been made on GIS environment with the help of estimated logistic regression co-efficient values to produce landslide susceptibility map of Darjeeling Himalaya. The produced susceptibility map satisfied the decision rules and the overall accuracy was acceptable. −2 Log likelihood, cox & Snell R Square, and Nagelkerke R Square values proved that the independent variables were statistically significant. The success rate curve showed the prediction accuracy of the landslide probability map which is also desirable (71.5).


GIS Logistic regression model Landslide conditioning factors Landslide susceptibility ROC curve Area under curve (AUC) 


  1. Ayalew, L., Yamagishi, H., Marui, H., & Kanno, T. (2005). Landslides in Sado island of Japan: part II, GIS-based susceptibility mapping with comparisons of results from two methods and verifications. Engineering Geology, 81, 432–445.CrossRefGoogle Scholar
  2. Bai, S., Lü, G., Wang, J., Zhou, P., & Ding, L. (2010). GIS-based rare events logistic regression for landslide-susceptibility mapping of Lianyungang, China. Environmental Earth Sciences, 62(1), 139–149.CrossRefGoogle Scholar
  3. Can, T., Nefeslioglu, H. A., Gokceoglu, C., Sonmez, H., & Duman, T. Y. (2005). Susceptibility assessments of shallow earth flows triggered by heavy rainfall at three catchments by logistic regression analyses. Geomorphology, 72, 250–271.CrossRefGoogle Scholar
  4. Crosta, G. (1998). Regionalization of rainfall thresholds: an aid to landslide hazard evaluation. Environmental Geology, 35, 131–145.CrossRefGoogle Scholar
  5. Dahal, R. K., & Hasegawa, S. H. (2008). Representative rainfall thresholds for landslides in the Nepal Himalaya. Geomorphology, 100, 429–443.CrossRefGoogle Scholar
  6. Dai, F. C., & Lee, C. F. (2002). Landslide characteristics and slope instability modelling using GIS, Lantau Island, Hong Kong. Geomorphology, 42, 213–228.CrossRefGoogle Scholar
  7. Das et al. (2012). Landslide susceptibility along road corridors in the Indian Himalaya using Bayesian logistic regression models. Geomorphology, 179, 116–125.CrossRefGoogle Scholar
  8. Demir, G., Aytekin, M., & Akgun, A. (2014). Landslide susceptibility mapping by frequency ratio and logistic regression methods: An example from Niksar–Resadiye (Tokat, Turkey). Arabian Journal of Geosciences.
  9. Dominguez-Cuesta, M., Jimenez-Sonchez, M., & Berrezueta, E. (2007). Landslide in the central coalfield (Cantabarian Mountains, NW Spain): Geomorphological feature conditioning factors and meteorological implication in susceptibility assessment. Geomorphology, 89, 1–12.CrossRefGoogle Scholar
  10. Falaschi et al. (2009). Logistic regression versus artificial neural networks: Landslide susceptibility evaluation in a sample area of the Serchio River valley, Italy. Natural Hazards, 50, 551–569.CrossRefGoogle Scholar
  11. Greco, R., Sorriso, V., & Catalano, E. (2007). Logistic regression analysis in the evaluation of mass movement’s susceptibility case study: Calabria, Italy. Engineering Geology, 89, 47–66.CrossRefGoogle Scholar
  12. Lee, S., & Pradhan, B. (2007). Landslide hazard mapping at Selangor Malaysia using frequency ratio and logistic regression models. Landslides, 4, 33–41.CrossRefGoogle Scholar
  13. Lee, S., & Sambath, T. (2006). Landslide susceptibility mapping in the DamreiRomel area, Cambodia using frequency ratio and logistic regression models. Environmental Geology, 50, 847–855.CrossRefGoogle Scholar
  14. McFadden, D. (1974). Conditional logit analysis of qualitative choice analysis. In P. Zarembka (Ed.), Frontiers in econometrics (pp. 105–142). New York: Academic Press.Google Scholar
  15. Menard, S. W. (Ed.). (2001). Applied logistic regression analysis (2nd ed., 111pp ed.). Thousand Oaks, CA: Sage.Google Scholar
  16. Mondal, S., & Mandal, S. (2017). RS & GIS-based landslide susceptibility mapping of the Balason River basin, Darjeeling Himalaya, using logistic regression (LR) model, Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 12(1),
  17. Nefeslioglu, H. A., Gokceoglu, C., & Sonmez, H. (2008). An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps. Engineering Geology, 97, 171–191.CrossRefGoogle Scholar
  18. Park et al. (2013). Landslide susceptibility mapping using frequency ratio, analytical hierarchy process, logistic regression and artificial neural network methods at the Inje Area, Korea. Environment and Earth Science, 68(5), 1443–1464.CrossRefGoogle Scholar
  19. Peart, M. R., Ng, K. Y., & Zhang, D. D. (2005). Landslides and sediment delivery to a drainage system: some observations from Hong Kong. Journal of Asian Earth Sciences, 25, 821–836.CrossRefGoogle Scholar
  20. Pourghasemi, H. R., Pradhan, B., & Gokceoglu, C. (2012). Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Natural Hazards, 63, 965–996.CrossRefGoogle Scholar
  21. Pourghasemi, H. R., et al. (2013). Landslide susceptibility mapping using certainty factor, index of entrophy and logistic regression models in GIS and their comparison at Mugling-Narayanghat road section in Nepal Himalaya. Natural Hazards, 65(1), 135–165.CrossRefGoogle Scholar
  22. Pradhan, B., & Lee, S. (2010). Regional landslide susceptibility analysis using back propagation neural network model at Cameron Highland, Malaysia. Landslides, 7, 13–30.CrossRefGoogle Scholar
  23. Soeters, R., & van Westen, C. J. (1996). Slope instability recognition, analysis and zonation. In: Turner, A. K., & Schuster, R. L. (Eds.), Landslides investigation and mitigation (pp. 129–177). Transportation Research Board, National Research Council, Special Report 247. Washington, DC: National Academy Press.Google Scholar
  24. Solaimani, K., Mousavi, S. Z., & Kavian, A. (2012). Landslide susceptibility mapping based on frequency ratio and logistic regression models. Arabian Journal of Geosciences, 6, 2557–2569. Scholar
  25. Wu, Z., Wu, Y., Yang, Y., Chen, F., Zhang, N., Ke, Y., & Li, W. (2017). A comparative study on the landslide susceptibility mapping using logistic regression and statistical index models. Arabian Journal of Geosciences, 10, 187. Scholar
  26. Yaclin, A. (2008). GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): Comparisons of results and confirmations. Catena, 72, 1–12.CrossRefGoogle Scholar
  27. Yesilnacar, E. K. (2005) The application of computational intelligence to landslide susceptibility mapping in Turkey. Ph.D Thesis, Department of Geomatics, the University of Melbourne, p. 423.Google Scholar

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© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Sujit Mandal
    • 1
  • Subrata Mondal
    • 2
  1. 1.Department of GeographyDiamond Harbour Women’s UniversitySarishaIndia
  2. 2.University of Gour BangaMokdumpurIndia

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