Frequency Ratio (FR) Model and Modified Information Value (MIV) Model in Landslide Susceptibility Assessment and Prediction

  • Sujit Mandal
  • Subrata Mondal


The assessment of landslide susceptibility is closely associated with the spatial distribution of landslides. In the present study, both frequency ratio (FR) model and modified information value (MIV) model were applied to analyse landslide susceptibility in Darjeeling Himalaya. Both the models dealt with the relationship between landslide phenomena and landslide conditioning factors. To perform the models data layers, 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. Each and every class/category of landslide conditioning factor contributes a relative importance in landslide occurrences. To prepare all the data layers, Landsat TM image, SRTM DEM, Google earth image, and some authorized maps were processed in accordance with ArcMap 10.1 and Erdas imagine 9.2. To obtain the relative significance of each class/category of landslide conditioning factors, frequency ratio (FR) value and modified information value (MIV) were estimated and accordingly the ranking values were assigned to each class/category to integrate all the data layers on GIS platform as well as to prepare landslide susceptibility map of Darjeeling Himalaya. The derived landslides susceptibility maps by using frequency ratio model and modified information value model were verified being considering the area under curve (AUC) of ROC curve and frequency ratio plot. The AUC value of ROC curve of FR model and MIV model was 0.746 and 0.769, respectively. The AUC value represents the prediction accuracy of landslide susceptibility map was 74.6% for frequency ratio model and 76.9% for modified information value model.


Darjeeling Himalaya Landslide susceptibility Frequency ratio (FR) model Modified information value (MIV) model ROC curve Model validation 


  1. Akbar T, Ha S (2011) Landslide hazard zoning along Himalaya KaghanValley of Pakistan-by integration of GPS, GIS, and remote sensingtechnology. Landslides, 8(4), 527–540.Google Scholar
  2. Arora, M., Das Gupta, A., & Gupta, R. (2004). An artificial neural network approach for landslide hazard zonation in the Bhagirathi (Ganga) Valley, Himalaya. International Journal of Remote Sensing, 25, 559–572. Scholar
  3. Avinash, K. G., & Ashamanjari, K. G. (2010). A GIS and frequency ratio based landslide susceptibility mapping: Aghnashini river catchment, Uttara Kannada, India. International Journal of Geomatics and Geosciences, 1(3), 343–354.Google Scholar
  4. Atkinson, P. M., & Massari, R. (1998). Generalized linear modelling of susceptibility to landsliding in the central Apennines, Italy. Computers & Geosciences, 24, 373–385.CrossRefGoogle Scholar
  5. Bagherzadeh, A., & Mansouri Daneshvar, M. R. (2012). Mapping of landslide hazard zonationusing GIS at Golestan watershed, northeast of Iran. Arabian Journal of Geosciences, 6, 3377–3388.CrossRefGoogle Scholar
  6. Balsubramani, K., & Kumaraswamy, K. (2013). Application of geospatial technology andinformation value technique in landslide hazard zonation mapping: A case study of Giri Valley, Himachal Pradesh. Disaster Advances, 6, 38–47.Google Scholar
  7. Bui, D. T., Lofman, O., Revhaug, I., & Dick, O. (2011). Landslide susceptibility analysis in the Hoa Binh province of Vietnam using statistical index and logistic regression. Natural Hazards, 59(3), 1413–1444.Google Scholar
  8. Caiyan WU and Jianping Q (2009) Relationship between landslides and lithology in the Three Gorges Reservoir area based on GIS AND Information Value Model. Higher Education Press and Springer New York 4(2), 165–170Google Scholar
  9. Champatiray, P. (2000). Perationalization of costeffective methodology for landslide hazard zonation using RS and GIS: IIRS initiative. In P. Roy, C. Van Westen, V. Jha, & R. Lakhera (Eds.), Natural disasters and their mitigation; remote sensing and geographical information system perspectives (pp. 95–101). Dehradun, India: Indian Institute of Remote Sensing.Google Scholar
  10. Champatiray, P., Dimri, S., Lakhera, R., & Sati, S. (2007). Fuzzy based methods for landslide hazard assessment in active seismic zone of Himalaya. Landslides, 4, 101–110. Scholar
  11. Choi, J., Oh, H. J., Lee, H. J., Lee, C., & Lee, S. (2011). Combining landslide susceptibility maps obtained from frequency ratio, logistic regression, and artificial neural network models using aster images and GIS. Engineering Geology, 124, 12–23.CrossRefGoogle Scholar
  12. Donati, L., & Turrini, M. C. (2002). An objective and 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(3-4), 277–289.CrossRefGoogle Scholar
  13. Ercanoglu, M., & Gokceoglu, C. (2004). Use of fuzzy relations to produce landslide susceptibility map of a landslide prone area (West Black Sea Region, Turkey). Engineering Geology, 75, 229–250.CrossRefGoogle Scholar
  14. 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. London: Elsevier.CrossRefGoogle Scholar
  15. Intarawichian, N., & Dasananda, S. (2011). Frequency Ratio model based landslide susceptibility mapping in lower Mae Chaem watershed, Northern Thailand. Environment and Earth Science, 64, 2271–2285.CrossRefGoogle Scholar
  16. Ilanloo, M. (2011). A comparative study of fuzzy logic approach for landslide susceptibility mapping using GIS: An experience of Karaj dam basin in Iran. Procedia - Social and Behavioral Sciences, 19, 668–676.CrossRefGoogle Scholar
  17. Jibson, W. R., Edwin, L. H., & John, A. M. (2000). A method for producing digital probabilistic seismic landslide hazard maps. Engineering Geology, 58, 271–289.CrossRefGoogle Scholar
  18. Kanungo, D., Arrora, M., Sarkar, S., & Gupta, R. (2009). Landslide Susceptibility Zonation (LSZ) mapping -A review. Journal of South Asia Disaster Studies, 2, 81–105.Google Scholar
  19. Karim, S., Jalileddin, S., & Ali, M. T. (2011). Zoning landslide by use of frequency ratio method (case study: Deylaman Region). Middle-East Journal of Scientific Research, 9(5), 578–583.Google Scholar
  20. Lee, S., Ryu, J. H., Lee, M. J., & Won, J. S. (2003). Use of an artificial neural network for analysis of the susceptibility to landslides at Boun, Korea. Environmental Geology, 44, 820–833.CrossRefGoogle Scholar
  21. Lee, S., & Pradhan, B. (2006a). Landslide hazard assessment at Cameron Highland Malaysia using frequency ratio and logistic regression models. Geophysical Research Abstracts, 8, SRef ID: 1607-7962/gra/EGU06-A-03241.Google Scholar
  22. 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
  23. Lee, S., & Talib, J. A. (2005). Probabilistic landslide susceptibility and factor effect analysis. Environmental Geology, 47, 982–990.CrossRefGoogle Scholar
  24. Lee, S., & Pradhan, B. (2006b). Probabilistic landslide risk mapping at Penang Island, Malaysia. Journal of Earth System Science, 115(6), 661–672.CrossRefGoogle Scholar
  25. Lee, S., & Pradhan, B. (2007). Landslide hazard mapping at Selangor, Malaysia using frequencyratio and logistic regression models. Landslides, 4(1), 33–41.CrossRefGoogle Scholar
  26. 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.
  27. Mandal, S., & Maiti, R. (2013). Integrating the Analytical Hierarchy Process (AHP) and the Frequency Ratio (FR) model in landslide susceptibility mapping of Shiv-khola Watershed, Darjeeling Himalaya. International Journal of Disaster Risk Science, 4(4), 200–212. Scholar
  28. Mandal, B., & Mandal, S. (2016). Assessment of mountain slope instability in the lish river basin of eastern Darjeeling Himalaya using frequency ratio model (FRM) Model. Earth Systems and Environment, 2(121), 1–14. Scholar
  29. Mandal, B., & Mandal, S. (2017). Landslide susceptibility mapping using modified information value model in the lish river basin of Darjiling Himalaya. Spatial Information Research, 2(7), 1–14. Scholar
  30. Muthu, K., & Petrou, M. (2007). Landslide hazard mapping using an ExpertSystem and a GIS. Transactions on Geoscience and Remote Sensing, 45(2), 522–531.CrossRefGoogle Scholar
  31. Nithya, E. S., & Prasanna, R. P. (2010). An integrated approach with GIS and remote sensing technique for landslide zonation. International Journal of Geomatics and Geosciences, 1(1), 66–75.Google Scholar
  32. Oliveira, S. C., Zêzere, J. L., Catalão, J., & Nico, G. (2015). The contribution of PSInSAR interferometry to landslide hazard in weak rocks dominated areas. Landslides, 12, 703–719.CrossRefGoogle Scholar
  33. Pandey, A., Dabral, P. P., Chowdhary, V. M., & Yadav, N. K. (2008). Landslide hazard zonation using remote sensing and GIS: A case study of Dikrong river basin, Arunachal Pradesh, India. Environmental Geology, 54, 1517–1529.CrossRefGoogle Scholar
  34. Pereira, S., Zezere, J., & Bateira, C. (2012). Assessing predictive capacity and conditional independence of landslide predisposing factors for shallow landslide susceptibility models. Natural Hazards and Earth System Sciences, 12, 979–988. Scholar
  35. Pistocchi, A., Luzi, L., & Napolitano, P. (2002). The use of predictive modelling techniques for optimal exploitation of spatial databases: A case study in landslide hazard mapping with expert system-like methods. Environmental Geology, 41, 765–775.CrossRefGoogle Scholar
  36. Poudyal, C. P., Chang, C., Oh, H. J., & Lee, S. (2010). Landslide susceptibility maps comparing frequency ratio and artificial neural networks: A case study from the Nepal Himalaya. Environmental Earth Sciences, 61, 1049–1064.CrossRefGoogle Scholar
  37. Pradhan, B., & Lee, S. (2010). Landslide susceptibility assessment and factor effect analysis: Backpropapagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling. Environmental Modelling & Software, 25, 747–759.CrossRefGoogle Scholar
  38. Porghasemi, H. (2007). Landslide hazard zoning statistical frequency ratio method in the basin Safarood. M.Sc Thesis, TarbiatModarres University, Noor, p. 1386.Google Scholar
  39. Pradhan, B. (2010). Remote sensing and GIS-based landslide hazard analysis and cross validation using multivariate logistic regression model on three test areas in Malaysia. Advances in Space Research, 45, 1244–1256.CrossRefGoogle Scholar
  40. Pradhan, B., & Lee, S. (2009). Delineation of landslide hazard areas using frequency ratio, logistic regression and artificial neural network model at Penang Island, Malaysia. Environmental Earth Sciences, 60, 1037–1054.CrossRefGoogle Scholar
  41. Rowbotham, D., & Dudycha, D. N. (1998). GIS Modelling of slope stability in Phewa Tal Watershed, Nepal. Geomorphology, 26, 151–170.CrossRefGoogle Scholar
  42. Sarkar, S., & Kanungo, D. P. (2004). An integrated approach for landslide susceptibility mapping using remote sensing and GIS. Photogrammetric Engineering & Remote Sensing, 70(5), 617–625.CrossRefGoogle Scholar
  43. Sarkar, S., Kanungo, D., Patra, A., & Kumar, P. (2006). Disaster mitigation of debris flows, slope failures and landslides: GIS based landslide susceptibility mapping case study in Indian Himalaya (pp. 617–624). Tokyo, Japan: Universal Academy Press.Google Scholar
  44. Sharma, L., Patel, N., Ghosh, M., & Debnath, P. (2009). Geographical information system based landslide probabilistic model with trivariate approach - A case study in Sikkim Himalaya. Eighteenth United Nations Regional Cartographic Conference for Asia and the Pacific, UN, Bankok, Economic and Social Council.Google Scholar
  45. Umar, Z., Pradhan, B., Ahmad, A., Jebur, M. N., & Tehrany, M. S. (2014). Earthquake induced landslide susceptibility mapping using an integrated ensemble frequency ratio and logistic regression models in West Sumatera Province, Indonesia. Catena, 118, 124–135.CrossRefGoogle Scholar
  46. Vijith, H., Rejith, P. G., & Madhu, G. (2009). Using Info Val Method and GIS techniques for the spatial modelling of landslides susceptibility in the Upper catchment of River Meenachil in Kerala. Indian Society of Remote Sensing, 37, 241–250.CrossRefGoogle Scholar
  47. Wang, H., & Sassa, K. (2005). Comparative evaluation of landslide susceptibility in Minamata area, Japan. Environmental Geology, 47, 956–966. Scholar
  48. Wang, Q., et al. (2015). Landslide susceptibility mapping based on selected optimal combination of landslide predisposing factors in a large catchment. Sustainability, 7, 16653–16669. Scholar
  49. Zezere, J. (2002). Landslide susceptibility assessment considering landslide typology: A case study in the area north of Lisbon (Portugal). Natural Hazards and Earth System Sciences, 2, 73–82. Scholar
  50. Zhou, C. H., Lee, C. F., Li, J., & Xu, Z. W. (2002). On the spatial relationship between landslide 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