Weighted Overlay Analysis (WOA) Model, Certainty Factor (CF) Model and Analytical Hierarchy Process (AHP) Model in Landslide Susceptibility Studies

  • Sujit Mandal
  • Subrata Mondal


The present study is dealt with the application of weighted overlay analysis (WOA) model, certainty factor (CF) model, analytical hierarchy process (AHP) model for the preparation of landslide susceptibility zonation map of Darjeeling Himalaya. To perform three models, various data layers with regard to 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. For the preparation of various data layers, topographical maps, Google earth images, SRTM DEM,, satellite image (Landsat TM) and some authorized data were being processed on GIS environment (ArcMap 10.1). The prepared landslide susceptibility maps of Darjeeling Himalaya were classified into five, i.e. very low, low, moderate, high, and very high landslide susceptibility. To validate three landslide susceptibility zonation maps derived from WOA, CF, and AHP models, ROC Curve and frequency ratio plot methods were incorporated. ROC curve showed the level of accuracy of each landslide susceptibility map. The study revealed that WOA, CF, and AHP were with the accuracy level of 65.4%, 81.2%, and 67.5%. Frequency ratio plots sugessted that moderate, high, and very high landslide susceptibility zones in Darjeeling Himalaya are experienced with greater probability landslide phenomena.


Landslide susceptibility Weighted overlay analysis model Certainty factor model Analytical hierarchy process ROC Curve Frequency ratio plot 


  1. Abramson, L. Y., Alloy, L. B., & Hogan, M. E. (1995). Cognitive/personality subtypes of depression: Theories in search of disorders. Cognitive Therapy and Research, 21, 247–265.CrossRefGoogle Scholar
  2. 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
  3. Basu, S. R., & Ghatowar, L. (1988). Landslide in the Lish Basin of the Eastern Himalayas and their control. In Geomorphology and environment. Allahabad: The Allahabad Geographical Society.Google Scholar
  4. Biaghi, E., Luzi, L., Mandella, P., Pergalani, F., & Rampini, A. (1998). Slope instability zonation: A comparison between certainty factor and fuzzydempster shafer approaches. Natural Hazars, 17, 77–97.CrossRefGoogle Scholar
  5. Chau, K. T., Sze, Y. L., Fung, M. K., Wong, W. Y., Fong, E. L., & Chan, L. C. P. (2004). Landslide hazard analysis for Hong Kong using landslide inventory and GIS. Computers & Geosciences, 30, 429–443.CrossRefGoogle Scholar
  6. Chung, C. F., & Fabbri, A. G. (1993). The representation of geoscience information for data integration. Nonrenewable Resources, 2(2), 122–139.CrossRefGoogle Scholar
  7. 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
  8. Donati, L., & Turrini, M. C. (2002). An objective method to rank, the importance of the factors predisposing to landslides with the GIS methodology: Application to an area of the Apennines (Valnerina; Perugia, Italy). Engineering Geology, 63, 277–289.CrossRefGoogle Scholar
  9. Dou, J., Oguchi, T., Hayakawa, Y. S., Uchiyama, S., Saito, H., & Paudel, U. (2014). GIS-based landslide susceptibility mapping using a certainty factor model and its validation in the Chuetsu area, central Japan. In Landslide science for a safer geoenvironment (pp. 419–424). New York: Springer.CrossRefGoogle Scholar
  10. Guzzetti, F., Carrara, A., Cardinali, M., & Reichenbach, P. (1999). Landslide hazard evaluation: A review of current techniques and their application in a multi-scale study, Central Italy. Journal of Geomorphology, 31, 181–216.CrossRefGoogle Scholar
  11. Heckeman. (1986). Probabilistic interpretation of MYCIN’s certainty factors. In L. N. Kanal & J. F. Lemmer (Eds.), Uncertainty in artificial intelligence B (pp. 298–311). New York: Elsevier.Google Scholar
  12. Ilia, I., Koumantakis, I., Rozos, D., Koukis, G., & Tsangaratos, P. (2015). A geographical information system (GIS) based probabilistic certainty factor approach in assessing landslide susceptibility: The case study of Kimi, Euboea, Greece. In Engineering geology for society and territory (Vol. 2, pp. 1199–1204). Berlin: Springer.CrossRefGoogle Scholar
  13. Jibson, R. W., Harp, E. L., & Michael, J. A. (2000). A method for producing digital probabilistic seismic landslide hazard maps. Engineering Geology, 58, 271–289.CrossRefGoogle Scholar
  14. Kanungo, D. P., Sarkar, S., & Sharma, S. (2011). Combining neural network with fuzzy, certainty factor and likelihood ratio concepts for spatial prediction of landslides. Natural Hazards, 59(3), 1491–1512.CrossRefGoogle Scholar
  15. Kienholz, H. (1978). Maps of geomorphology and natural hazards of Grindelwald, Switzerland: Scale 1:10,000. Arctic and Alpine Research, 10, 169–184.CrossRefGoogle Scholar
  16. Komac, M. (2006). A landslide susceptibility model using the Analytical Hierarchy Process method and multivariate statistics in peialpine Slovenia. Geomorphology, 74, 17–28.CrossRefGoogle Scholar
  17. Liu, M., Chen, X., & Yang, S. (2014). Collapse landslide and mudslide hazard zonation. In Landslide science for a safer geoenvironment (pp. 457–462). Berlin: Springer.CrossRefGoogle Scholar
  18. Malczewski, J. (1999). GIS and multi-criteria decision analysis (p. 392). New York: Wiley.Google Scholar
  19. Mandal, S., & Maiti, R. (2011). Landslide susceptibility analysis of Shivkhola Watershed, Darjeeling: A remote sensing & GIS Based Analytical Hierarchy Process (AHP). Journal of Indian Society of Remote Sensing.
  20. Moradi, M., Bazyar, M. H., & Mohammadi, Z. (2012). GIS-based landslide susceptibility mapping by AHP method: A case study, Dena City, Iran. Journal of Basic and Applied Scientific Research, 2, 6715–6723.Google Scholar
  21. Pourghasemi, H. R., Pradhan, B., Gokceoglu, C., Mohammadi, M., & Moradi, H. R. (2013). Application of weights-of-evidence and certainty factor models and their comparison in landslide susceptibility mapping at Haraz Watershed, Iran. Arabian Journal of Geosciences, 6(7), 2351–2365.CrossRefGoogle Scholar
  22. Praise, M., & Jibson, R. W. (2000). A seismic landslide susceptibility rating of geologic units based on analysis of characteristics of landslide triggered by the January 17, 1994 Northridge, California, earthquake. Engineering Geology, 58, 251–270.CrossRefGoogle Scholar
  23. Rautelal, P., & Lakheraza, R. C. (2000). Landslide risk analysis between Giri and Tons Rivers in Himachal Himalaya, India. International Journal of Applied Earth Observation and Geoinformation, 2, 153–160.CrossRefGoogle Scholar
  24. Saaty, T. L. (1977). A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology, 15, 234–281.CrossRefGoogle Scholar
  25. Saaty, T. L. (1980). The analytical hierarchy process. New York: McGraw-Hill.Google Scholar
  26. Saaty, T. L. (1994). Fundamentals of decision making and priority theory with analytic hierarchy process. Pittsburgh: RWS Publications.Google Scholar
  27. Saaty, T. L., & Vargas, G. L. (1991). Prediction, projection and forecasting. Dordrecht: Kluwer Academic.CrossRefGoogle Scholar
  28. Saaty, T. L., & Vargas, G. L. (2001). Models, methods, concepts and applications of the analytic hierarchy process. Dordrecht: Kluwer.CrossRefGoogle Scholar
  29. Shortliffe, E. H., & Buchanan, B. G. (1975). A model of inexact reasoning in medicine. Mathematical Biosciences, 23(3), 351–379.CrossRefGoogle Scholar
  30. Spiker, E. C., & Gori, P. L. (2000). National landslide hazards mitigation strategy: A framework for loss reduction (p. 59). Reston: Department of the Interior, U.S. Geological Survey.Google Scholar
  31. Sujatha, E. R., Rajamanickam, G. V., & Kumaravel, P. (2012). Landslide susceptibility analysis using probabilistic certainty factor approach: A case study on Tevankarai stream watershed, India. Journal of Earth System Sciences, 121(5), 1337–1350.CrossRefGoogle Scholar
  32. Varnes, D. J. (1978). Slope move types and processes. In R. L. Schuster & R. J. Krized (Eds.), Landslide analysis and control (pp. 12–33). New York: National Academy of Science.Google Scholar
  33. Varnes, D. J. (1981). Slope stability problems of the circum Pacific region as related to mineral and energy resource. In M. T. Halbouty (Ed.), Energy resources of the Pacific region. American Association of Petroleum Geologists Studies in Geology. No. 12 (pp. 489–505). Tulsa, Okla: American Association of Petroleum Geologist.Google Scholar
  34. Yalcin, A. (2008). GIS-based landslide susceptibility mapping using analytical hierarchyprocess and bivariate statistics in Ardesen (Turkey): Comparisons of results andconfirmations. Catena, 72, 1–12.CrossRefGoogle Scholar
  35. Zhou, C. H., Lee, C. F., Li, J., & Xu, Z. W. (2002). On the spatial relationship between landslides and causative factors on Lantau Island, Hong Kong. Geomorphology, 43, 197–207.CrossRefGoogle Scholar

Copyright information

© 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

Personalised recommendations