Landslide prediction capability by comparison of frequency ratio, fuzzy gamma and landslide index method

  • Mahvash Gholami
  • Esmaeil Nekouei Ghachkanlu
  • Khabat Khosravi
  • Saied PirastehEmail author


This study presented the prediction capability of three methods including the frequency ratio (FR), fuzzy gamma (FG) and landslide index method (LIM) to produce landslide-prone areas in the Sari-Kiasar watershed, Mazandaran Province of Iran. In the first step, 105 landslide locations were selected and were randomly divided into two groups of 75% (78 locations) and 25% (27 locations) as training and testing datasets. Then the 17 landslide conditioning factors including land use/land cover, Differential Vegetation Index (DVI), lithology and distance from faults, elevation, slope aspect, slope angle, tangential curvature, profile curvature and plane curvature, distance from drainage, rainfall, Stream Power Index, Sediment Transport Index and temperature, and distance from road, density of settlement were considered for the proposed modelling approach. Finally, by applying the training dataset, three landslide susceptibility maps were constructed by using the FR, FG and LIM methods. The prediction capability of the performed model was evaluated by the area under the receiver operating curve or AUC for both training (success rate) and testing (prediction rate) datasets. The results showed that the AUC for success rate of FR, FG and LIM models was 82.04%, 81.08% and 73.61% and for prediction rate was 82.72%, 79.09% and 65.45%, respectively. The results showed that the FR model has a higher prediction accuracy than the FG and LIM methods. This study revealed that the most important factors in landslide occurrence are rainfall, slope and vegetation. The result of the present study can be possibly useful for land use planning and watershed management.


Landslide susceptibility FR fuzzy gamma LIM Sari-Kiasar 



The authors are thankful to the Mazandaran Regional Water Authority and the Natural Resources Bureau of Mazandaran for providing data to accomplish this study.


  1. Abul Hasanat M, Ramachandram D and Mandava R 2010 Bayesian belief network learning algorithms for modeling contextual relationships in natural imagery: A comparative study; Artif. Intell. Rev. 34 291–308.CrossRefGoogle Scholar
  2. Ahmed S 2009 Slope stability analysis using GIS and numerical modeling techniques (unpublished M.Sc. thesis), Vrije Universiteit, Brussel.Google Scholar
  3. Akgun A and Bulut F 2007 GIS-based landslide susceptibility for Arsin-Yomra (Trabzon, North Turkey) region; Environ. Geol. 51(8) 1377–1387.CrossRefGoogle Scholar
  4. Akgun A and Turk N 2010 Landslide susceptibility mapping for Ayvalik (Western Turkey) and its vicinity by multicriteria decision analysis; Environ. Earth Sci. 61(3) 595–611.CrossRefGoogle Scholar
  5. Althuwaynee O F, Pradhan B and Lee S 2012 Application of an evidential belief function model in landslide susceptibility mapping; Comput. Geosci. 44 120–135.CrossRefGoogle Scholar
  6. Althuwaynee O F, Pradhan B, Park H J and Lee J H 2014 A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping; Catena 114 21–36.CrossRefGoogle Scholar
  7. An P, Moon W and Rencz A 1991 Application of fuzzy set theory for integration of geological, geophysical and remote sensing data; Can. J. Explor. Geophys. 27 1–11.Google Scholar
  8. Ayalew L, Yamagishi H and Ugawa N 2004 Landslide susceptibility mapping using GIS-based weighted linear combination, the case in Tsugawa area of Agano River, Niigata Prefecture, Japan; Landslides 1 73–81.CrossRefGoogle Scholar
  9. Ayalew L, Yamagishi H, Marui H and Kanno T 2005 Landslides in Sado Island of Japan: Part II. GIS – based susceptibility mapping with comparisons of results from two methods and verifications; Eng. Geol. 81 432–445.CrossRefGoogle Scholar
  10. Barrile V, Cirianni F, Leonardi G and Palamara R 2016 A fuzzy-based methodology for Landslide Susceptibility Mapping; Procedia Soc. Behav. Sci. 223 896–902.CrossRefGoogle Scholar
  11. Caniani D, Pascale S, Sdao F and Sole A 2008 Neural networks and landslide susceptibility: A case study of the urban area of Potenza; Nat. Hazards 45 55–72.CrossRefGoogle Scholar
  12. Carson M A and Kirkby M J 1972 Hillslope form and process; Cambridge University Press, New York, Vol. 178(4065), pp. 1083–1084,
  13. Cascini L, Cuomo S and Sala M D 2011 Spatial and temporal occurrence of rainfall-induced shallow landslides of flow type: A case of Sarno-Quindici, Italy; Geomorphology 126(1–2) 148–158, Scholar
  14. Cevik E and Topal T 2003 GIS-based landslide susceptibility mapping for a problematic segment of the natural gas pipeline, Hendek (Turkey); Environ. Geol. 44 949–962.CrossRefGoogle Scholar
  15. Chang S H and Wan S 2014 Discrete rough set analysis of two different soil behavior induced landslides in National Shei-Pa Park, Taiwan; Geosci. Front.,
  16. Chou W C, Lin W T and Lin C Y 2009 Vegetation recovery patterns assessment at landslides caused by catastrophic earthquake: A case study in central Taiwan; Environ. Monit. Assess. 152(1–4) 245–257.CrossRefGoogle Scholar
  17. Chung C and Fabbri A 2001 Prediction models for landslide hazard zonation using a fuzzy set approach; Geomorphology and Environmental Impact Assessment Balkema, Lisse, The Netherlands, pp. 31–47.Google Scholar
  18. Chung C J F and Fabbri A G 2003 Validation of spatial prediction models for landslide hazard mapping; Nat. Hazards 30(3) 451–472.CrossRefGoogle Scholar
  19. Comegna L, Picarelli L, Bucchignani E and Mercogliano P 2013 Potential effects of incoming climate changes on the behavior of slow active landslides in clay; Landslides 10(4) 373–391, Scholar
  20. Conforti M, Pascale S, Robustelli G and Sdao F 2013 Evaluation of prediction capability of the artificial neural networks for mapping landslide susceptibility in the Turbolo river catchment (northern Calabria, Italy); Catena,
  21. Crosby D A 2006 The effect of DEM resolution on the computation of hydrologically significant topographic attributes; M. S. Thesis Arts, Department of geography, College of Arts and Sciences, University of South Florida, 135p.Google Scholar
  22. Crozier M J 2010 Deciphering the effect of climate change on landslide activity: A review; Geomorphology 124 260–267.CrossRefGoogle Scholar
  23. Eastman J R 2003 IDRISI Kilimanjaro: Guide to GIS and image processing; Clark Labs, Clark University, Worcester, 328p.Google Scholar
  24. Eshghabad S M, Solaimani K and Omidvar E 2012 Landslide susceptibility mapping using multiple regression and GIS tools in Tajan Basin, north of Iran; Environ. Nat. Res. Res. 2(3).Google Scholar
  25. Evans I S 1979 An integrated system of terrain analysis and Slope mapping; Final Report on Grant DA-ERO-591-73-G0040, University of Durham, Durham, UK.Google Scholar
  26. Farrokhnia A, Pirasteh S, Biswajeet P, Pourkermani M and Arian M 2011 A recent scenario of mass wasting and its impact on the transportation in Alborz Mountains, Iran: Contribution from Geo information technology; Arabian Geosci. J. 4 1337–1349.CrossRefGoogle Scholar
  27. Glade T and Crozier M J 2005 A review of scale dependency in landslide hazard and risk analysis; Landslide Hazard Risk,
  28. Hong H, Xu C and Bui D T 2015 Landslide susceptibility assessment at the Xiushui area (China) using frequency ratio model; Procedia Earth Planet. Sci. 15 513–517, Scholar
  29. 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 Soc. Behav. Sci. 19 668–676.CrossRefGoogle Scholar
  30. Jaafari A, Najafi A, Pourghasemi H R, Rezaeian J and Sattarian A 2014 GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran; Int. J. Environ. Sci. Technol. 11(4) 909–926.CrossRefGoogle Scholar
  31. Khosravi K, Nohani E, Maroufinia E and Pourghasemi H R 2016a A GIS-based flood susceptibility assessment and its mapping in Iran: A comparison between frequency ratio and weights-of-evidence bivariate statistical models with multi-criteria decision-making technique; Nat. Hazards 83(2) 1–41.CrossRefGoogle Scholar
  32. Khosravi K, Pourghasemi H R, Chapi K and Bahri M 2016b Flash flood susceptibility analysis and its mapping using different bivariate models in Iran: A comparison between Shannon’s entropy, statistical index, and weighting; Environ. Monit. Assess. 188(12) 656.CrossRefGoogle Scholar
  33. Koukis G and Ziourkas C 1991 Slope instability phenomena in Greece: A statistical analysis; Bull. Int. Assoc. Eng. Geol. 43 47–60.CrossRefGoogle Scholar
  34. Lee S and Dan N T 2005 Probabilistic landslide susceptibility mapping in the Lai Chau province of Vietnam: Focus on the relationship between tectonic fractures and landslides; Environ. Geol. 48(6) 778–787.CrossRefGoogle Scholar
  35. Lee S and Pradhan B 2006 Probabilistic landslide hazards and risk mapping on Penang Island, Malaysia; J. Earth Syst. Sci. 115 661–672.CrossRefGoogle Scholar
  36. Lee S and Sambath T 2006 Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models; Environ. Geol. 50 847–855.CrossRefGoogle Scholar
  37. Lee S and Pradhan B 2007 Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models; Landslides 4 33–41.CrossRefGoogle Scholar
  38. Lee Y F and Chi Y Y 2011 Rainfall-induced landslide risk at Lushan, Taiwan; Eng. Geol. 123 113–121, Scholar
  39. Lefteri H T and Robert E U 1997 Fuzzy and neural approaches in engineering; A Wiley Interscience Publication.Google Scholar
  40. Li L, Liu R, Pirasteh S, Chen X, Long H and Li J 2017 A novel genetic algorithm for optimization of conditioning factors in shallow translational landslides and susceptibility mapping; Arab. J. Geosci. 10 209, Scholar
  41. Meteorological Organization of Mazandaran Province 2013 Long-term report of synoptic stations.Google Scholar
  42. Metz C E 1978 Basic principles of ROC analysis; Semin. Nucl. Med. 8 283–298.CrossRefGoogle Scholar
  43. Moore I D and Burch G J 1986 Sediment transport capacity of sheet and rill flow: Application of unit stream power theory; Water Resour. Res. 22 1350–1360.CrossRefGoogle Scholar
  44. Moore I D and Wilson J P 1992 Length-slope factors for the revised universal soil loss equation: Simplified method of estimation; J. Soil Water Conserv. 47(5) 423–428.Google Scholar
  45. Moore I, Grayson R and Ladson A 1991 Digital terrain modeling: A review of hydrological, geomorphological, and biological applications; Hydrol. Process. 5 3–30.CrossRefGoogle Scholar
  46. Morrison A M 2005 Receiver operating characteristic (ROC) curve preparation: A tutorial; Boston, Massachusetts Water Resources Authority, Report ENQUAD 2005-20, 5p.Google Scholar
  47. Nezhadali E, Ouri A E and Pazira E 2013 Evaluation and Landslide hazard zonation using LIM model with GIS techniques (case study: Saein watershed. Ardabil); Int. J. Farming Allied Sci., ISSN 2322–4134.Google Scholar
  48. Ozdemir H and Turoglu H 2007 Landslide susceptibility assessment using GIS and RS in the Havran river Basin (Balikesir-Turkey); In: 12th Conference of International Association for Mathematical Geology, pp. 26–31.Google Scholar
  49. Ozdemir A and Altural T 2013 A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey; J. Asian Earth Sci. 64(5) 180–197.CrossRefGoogle Scholar
  50. Park S, Choi C, Kim B and Kim J 2013 Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea; Environ. Earth Sci. 68(5) 1443–1464.CrossRefGoogle Scholar
  51. Pham B T, Bui D T, Pourghasemi H R, Indra P and Dholakia M B 2017 Landslide susceptibility assessment in the Uttarakhand area (India) using GIS: A comparison study of prediction capability of naïve Bayes, multilayer perceptron neural networks, and functional trees methods; Theor. Appl. Climatol. 1–19,
  52. Pirasteh S and Li J 2016 Landslides investigations from geo-informatics perspective: Quality, challenges, and recommendations; Geomatics Nat. Hazards Risk 1–18,
  53. Pirasteh S and Li J 2017a Probabilistic frequency ratio (PFR) model for quality improvement of landslides susceptibility mapping from LiDAR point clouds; Geoenviron. Disaster J. 4–19,
  54. Pirasteh S and Li J 2017b Global Changes and Natural Disaster Management: Geo-information Technologies; Springer, Berlin, ISBN 978-3-319-51843-5.CrossRefGoogle Scholar
  55. Pirasteh S, Jonathan L and Michael C 2017 Use of LiDAR-derived DEM and a stream length-gradient index approach to investigation of landslides in Zagros mountains, Iran; Geocarto Int. J.,
  56. Pourghasemi H R, Pradhan B and Gokceoglu C 2012a Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran; Nat. Hazards 63(2) 965–996.CrossRefGoogle Scholar
  57. Pourghasemi H R, Mohammady M and Pradhan B 2012b Landslide susceptibility mapping using index of entropy and conditional probability models in GIS: Safarood Basin, Iran; Catena 97 71–84.CrossRefGoogle Scholar
  58. Pourghasemi H R, Jirandeh A G, Pradhan B, Xu C and Gokceoglu C 2013 Landslide susceptibility mapping using support vector machine and GIS at the Golestan Province, Iran; J. Earth Syst. Sci. 122(2) 349–369.CrossRefGoogle Scholar
  59. Pradhan B, Oh H J and Buchroithner M 2010 Weights of evidence model applied to landslide susceptibility mapping in a tropical hilly area; Geomat. Nat. Hazards 1(3) 199–223.CrossRefGoogle Scholar
  60. Raghuvanshi T K, Negassa L and Kala P M 2015 GIS based grid overlay method vs. modeling approach: A comparative study for landslide hazard zonation (LHZ) in Meta Robi District of West Showa Zone in Ethiopia; Egypt. J. Remote Sens. Space Sci. 18 235–250.Google Scholar
  61. Rahmati O and Pourghasemi H R 2017 Identification of critical flood prone areas in data-scarce and ungauged regions: A comparison of three data mining models; Water Resour. Manag. 31(5) 1473–1487.CrossRefGoogle Scholar
  62. Ramesh V, Phaomei T, Baskar M and Anbazhagan S 2016 Application of fuzzy gamma operator in landslide susceptibility mapping along Yercaud Ghat road section, Tamil Nadu, India; In: Geostatistical and Geospatial Approaches for the Characterization of Natural Resources in the Environment (ed.) Raju N, Springer,
  63. Regmi A D, Devkota K C, Yoshida K, Pradhan B, Pourghasemi H R, Kumamoto T and Akgun A 2013 Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya; Arab. J. Geosci. 7(2) 725–742.CrossRefGoogle Scholar
  64. Renard K G, Foster G R, Weesies G A, Mccool D K and Yoder D C 1997 Predicting soil erosion by water: A guide to conservation planning with the revised soil loss equation (RUSLE); US Dept. of Agriculture, Agriculture Handbook No. 703, 404p.Google Scholar
  65. Rosenfeld C L 1994 The geomorphological dimensions of natural disasters; Geomorphology 10(1) 27–36.CrossRefGoogle Scholar
  66. Ruff M and Czurda K 2008 Landslide susceptibility analysis with a heuristic approach in the Eastern Alps (Vorarlberg, Austria); Geomorphology 94(3–4) 314–324.CrossRefGoogle Scholar
  67. Safari A and Moghimi E 2009 Geomorphologic assessment of urban development and vulnerability caused by landslide in mountainous hillsides of Tehran metropolis; Phys. Geogr. Res. Q. 67 53–71.Google Scholar
  68. Saha A K, Gupta R P and Arora M K 2002 GIS-based landslide hazard zonation in the Bhagirathi (Ganga) valley, Himalayas; Int. J. Remote Sens. 23 357–369.CrossRefGoogle Scholar
  69. Sahin E K, Ipbuker C and Kavzoglu T 2015 A comparison of feature and expert-based weighting algorithms in landslide susceptibility mapping; Procedia Earth Planet. Sci. 15 462–467.CrossRefGoogle Scholar
  70. Santos J G 2013 GIS-based hazard and risk maps of the Douro river basin (north-eastern Portugal); Geomat. Nat. Hazards Risk 2 90–114.Google Scholar
  71. Senatore A, Mendicino G, Smiatek G and Kunstmann H 2011 Regional climate change projections and hydrological impact analysis for a Mediterranean basin in Southern Italy; J. Hydrol. 399 70–92, Scholar
  72. Shahabi H, Khezri S, Bin Ahmad B and Hashim M 2014 Landslide susceptibility mapping at central Zab basin, Iran: A comparison between analytical hierarchy process, frequency ratio and logistic regression models; Catena 115 55–70.CrossRefGoogle Scholar
  73. Sujatha E R, Rajamanickam G V and Kumaravel P 2012 Landslide susceptibility analysis using probabilistic certainty factor approach: A case study on Tevankarai stream watershed, India; Environ. Earth Sci. 120(5) 1337–1350.Google Scholar
  74. Tien Bui D, Pradhan B, Lofman O, Revhaug I and Dick O B 2012 Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS; Comput. Geosci. 45 199–211.CrossRefGoogle Scholar
  75. Vakhshoori V and Zare M 2016 Landslide susceptibility mapping by comparing weight of evidence, fuzzy logic, and frequency ratio methods; Geomat. Nat. Hazards Risk 5 1731–1752.CrossRefGoogle Scholar
  76. Van Westen C J 1997 Statistical landslide hazard analysis; Application guide, ILWIS 2.1 for Windows. ITC, Enschede, the Netherlands, pp. 73–84.Google Scholar
  77. Varnes D J 1978 Slope movements: Types and processes; In: Landslide Analysis and Control, National Academy of Sciences (eds) Schuster R L and Krizek R J, Transportation Research Board Special Report, Vol. 176, pp. 11–33.Google Scholar
  78. Wilson J P and Gallant J C 2000 Digital terrain analysis; In: Terrain Analysis: Principles and Applications (eds) Wilson J P and Gallant J C, Wiley, New York, pp. 1–27.Google Scholar
  79. Wu Y, Chen L, Cheng C, Yin K and Torok A 2014 GIS-based landslide hazard predicting system and its real-time test during a typhoon, Zhejiang Province, Southeast China; Eng. Geol. 175 9–21.CrossRefGoogle Scholar
  80. Yalcin 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
  81. Yalcin A, Reis S, Aydinoglu A C and Yomralioglu T 2011 A GIS-based comparative study of frequency ratio, analytical hierarchy process, bivariate statistics and logistics regression methods for landslide susceptibility mapping in Trabzon, NE Turkey; Catena 85(3) 274–287.CrossRefGoogle Scholar
  82. Ye C, Cui P, Zhang J, Li J, Meng Q, Bi X and Pirasteh S 2016 GiT-based structural geologic feature analysis of the southern segment of Longmenshan Fault Zone for earthquake evidence; J. Mountain Sci. 13(5) 906–916, Scholar
  83. Yilmaz I 2009 Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat Turkey); Comput. Geosci. 35 1125–1138.CrossRefGoogle Scholar
  84. Yusof N, Ramli M F, Pirasteh S and Shafri H Z M 2011 Landslides and lineaments mapping along the Simpang Pulai to Kg Raja highway, Malaysia; Int. J. Remote Sens. 32(14) 4089–4105.CrossRefGoogle Scholar
  85. Zadeh L A 1965 Fuzzy sets; IEEE Inf. Control 8 338–353.CrossRefGoogle Scholar
  86. Zevenbergen L W and Thorne C R 1987 Quantitative analysis of land surface topography; Earth Surf. Process. Landf. 12(1) 47–56.CrossRefGoogle Scholar

Copyright information

© Indian Academy of Sciences 2019

Authors and Affiliations

  • Mahvash Gholami
    • 1
  • Esmaeil Nekouei Ghachkanlu
    • 2
  • Khabat Khosravi
    • 3
  • Saied Pirasteh
    • 4
    Email author
  1. 1.Department of Watershed Management Engineering, College of Natural Resources and Marine SciencesTarbiat Modares UniversityNoorIran
  2. 2.Department of Earth Sciences, College of SciencesShiraz UniversityShirazIran
  3. 3.Department of Watershed Management EngineeringSari Agricultural Science and Natural Resources UniversitySariIran
  4. 4.Department of Surveying and GeoinformaticsFaculty of Geosciences and Environmental Engineering (FGEE), Southwest Jiaotong UniversityChengduChina

Personalised recommendations