Modeling Earth Systems and Environment

, Volume 4, Issue 2, pp 685–698 | Cite as

Fuzzy logic approach for landslide hazard zonation mapping using GIS: a case study of Nilgiris

  • S. Uvaraj
  • R. Neelakantan
Original Article


Landslides are one of the common natural as well as man-made hazards in mountainous terrain which causes huge damage to human beings and property. In this paper mapping of landslide hazardous zones based on fuzzy logic approach using GIS techniques. In this regard various landslide parameters were generated which related to landslide occurrences in Coonoor and Kothagiri taluks. The fuzzy membership value is assigned on individual parameters such as active–passive slope, concave-plain-convex slope, drainage density, dissected-un dissected slope, geology, geomorphology, lineament density, lineament frequency, lineament intersection density, landuse/land cover, rainfall, degree of weathering /regolith cover, shallow-moderate-steep slope, soil, and water level. An attempt is made to integrate these parameters on different fuzzy operators and produce landslide hazard zonation map and its divides the study area into three zones viz., high, moderate and low. 95% of the existing landslides have been observed in high hazard class. The final map is validated using area under curve method for calculating prediction accuracy.


Fuzzy membership Landslide hazard zonation Fuzzy operators AUC method 



The first author would like to thank the University Grants Commission, New Delhi for financial support for this part of Ph.D. work under the scheme of RGNF. The authors gratefully acknowledge anonymous reviewers for their constructive comments which significantly improved the quality of the paper.


  1. Abdul Rahaman S, Aruchamy S (2017) Geoinformatics based landslide vulnerable zonation mapping using analytical hierarchy process (AHP), a study of Kallar river sub watershed, Kallar watershed, Bhavani basin, Tamil Nadu. Model Earth Syst Environ 3:41CrossRefGoogle Scholar
  2. Akgun A, Sezer EA, Nefeslioglu HA, Gokceoglu C, Pradhan B (2012) An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm. Comput Geosci 38(1):23–34CrossRefGoogle Scholar
  3. Begueria S (2006) Validation and evaluation of predictive models in hazard assessment and risk management. Nat hazards 37:315–329CrossRefGoogle Scholar
  4. Bonham-Carter GF (1994) Geographic information systems for geoscientists: modelling with GIS. Elsevier Butterworth-Heine-mann, Oxford, pp 292–302Google Scholar
  5. Biplab Mandal and Sujit Mandal (2016) Assessment of mountain slope instability in the Lish River basin of Eastern Darjeeling Himalaya using frequency ratio model (FRM). Model Earth Syst Environ 2:121CrossRefGoogle Scholar
  6. Carranza EJM, Hale M (2001) Geologically constrained fuzzy mapping of gold mineralization potential, Baguio District, Philippines. Nat Resour Res 10(2):125–136CrossRefGoogle Scholar
  7. Carrara A (1983) Multivariate models for landslide hazard evaluation. Math Geol 15:403–426CrossRefGoogle Scholar
  8. Carrara A, Cardinali M, Guzzetti F, Reichenbach P (1995) GIS technology in mapping landslide hazard. In: Carrara A, Guzzetti F (eds) Geographical information systems in assessing natural hazards. Kluwer Academic Publisher, Dordrecht, pp 135–175CrossRefGoogle Scholar
  9. Champati Ray PK, Dimri S, Lakhera RC, Sati S (2007) Fuzzy-based method for landslide hazard assessment in active seismic zone of Himalaya. Landslides, 4:101–111Google Scholar
  10. Cheng Q, Agterberg FP (1999) Fuzzy weights of evidence method and its application in mineral potential mapping. Nat Resour Res 8:27–35CrossRefGoogle Scholar
  11. Chung CF, Fabbri AG (1993) The representation of geoscience information for data integration. Nonrenew Resour 2(2):122–139CrossRefGoogle Scholar
  12. Dai FC, Lee CF, Ngai YY (2002) Landslide risk assessment and management: an overview. Eng Geol 64(1):65–87. CrossRefGoogle Scholar
  13. Das I, Sahoo S, Van Weston C, Stein A, Heck R (2010) Landslide susceptibility using logistic regression and its comparison with a rock mass classification system, along road section in the northern Himalayas (India). Geomorphology 114:627–663CrossRefGoogle Scholar
  14. Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27:861–874CrossRefGoogle Scholar
  15. Gorsevski PV, Gessler PE, Jankowski P (2003) Integrating a fuzzy k-means classification and a Bayesian approach for spatial prediction of landslide hazard. J Geogr Syst 5:223–251CrossRefGoogle Scholar
  16. Guzzetti F, Reichenbach P, Cardinali M, Galli M, Ardizzone F (2005) Landslide hazard assessment in the Staffora basin. North Ital Apennines Geomorphol 72:272–299Google Scholar
  17. Hinotoli V, Sema B, Guru R Veerappan (2017) Fuzzy gamma operator model for preparing landslide susceptibility zonation mapping in parts of Kohima Town, Nagaland, India. Model Earth Syst Environ Volume 3(2):499–514CrossRefGoogle Scholar
  18. Lee S (2007) Application and verification of fuzzy algebraic operators to landslide susceptibility mapping. Environ Geol 52:615–623. CrossRefGoogle Scholar
  19. Menggenang P, Samanta S (2017) Modelling and mapping of landslide hazard using remote sensing and GIS techniques. Model Earth Syst Environ 3, 3, 1113–1122CrossRefGoogle Scholar
  20. Neelakantan R, Yuvaraj S (2013) Relative effect-based landslide hazard zonation mapping in parts of Nilgiris, Tamil Nadu, South India. Arab J Geosci 6(11):4207–4213CrossRefGoogle Scholar
  21. Pradhan B (2011a) Manifestation of an advanced fuzzy logic model coupled with geoinformation techniques to landslide susceptibility mapping and their comparison with logistic regression modelling. Environ Ecol Stat 18(3):471–493CrossRefGoogle Scholar
  22. Pradhan B (2011b) Use of GIS-based fuzzy logic relations and its cross application to produce landslide susceptibility maps in three test areas in Malaysia. Environ Earth Sci 63:329–349CrossRefGoogle Scholar
  23. Pradhan B, Lee S (2009) Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models. Environ Earth Sci 60:1037–1054CrossRefGoogle Scholar
  24. Shit PK, Bhunia GS, Maiti R (2016) Potential landslide susceptibility mapping using weighted overlay model (WOM). Model Earth Syst Environ 2:21CrossRefGoogle Scholar
  25. Singh R, Umrao RK, Singh TN (2013) Probabilistic analysis of slope in Amiyan landslide area, Uttarakhand. Geomat Nat Haz Risk 4(1):13–29. CrossRefGoogle Scholar
  26. Van Asch TWJ, Buma J, Van Beek LPH (1999) A view on some hydrological triggering systems in landslides. Geomorphology 30:25–32CrossRefGoogle Scholar
  27. Van Westen CJ (1993) Application of geographic information systems to landslide hazard zonation. The Netherlands, International Institute for Aerospace Survey and Earth Sciences, Enschede, p 245Google Scholar
  28. Yilmaz I (2009) A case study from Koyulhisar (Sivas-Turkey) for landslide susceptibility mapping by artificial neural networks. B Eng Geol Environ 68(3):297–306. CrossRefGoogle Scholar
  29. Zimmermann HJ, Zysno P (1980) Latent connectives in human decision making. Fuzzy Set Syst 4:37–51CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Industries and Earth SciencesTamil UniversityThanjavurIndia

Personalised recommendations