Journal of Mountain Science

, Volume 15, Issue 4, pp 808–824 | Cite as

Hazard assessment of landslide disaster using information value method and analytical hierarchy process in highly tectonic Chamba region in bosom of Himalaya

  • Kanwarpreet Singh
  • Virender Kumar


The present study is focused on a comparative evaluation of landslide disaster using analytical hierarchy process and information value method for hazard assessment in highly tectonic Chamba region in bosom of Himalaya. During study, the information about the causative factors was generated and the landslide hazard zonation maps were delineated using Information Value Method (IV) and Analytical Hierarchy Process (AHP) using ArcGIS (ESRI). For this purpose, the study area was selected in a part of Ravi river catchment along one of the landslide prone Chamba to Bharmour road corridor of National Highway (NH-154A) in Himachal Pradesh, India. A numeral landslide triggering geoenvironmental factors i.e. slope, aspect, relative relief, soil, curvature, land use and land cover (LULC), lithology, drainage density, and lineament density were selected for landslide hazard mapping based on landslide inventory. Landslide hazard zonation map was categorized namely “very high hazard, high hazard, medium hazard, low hazard, and very low hazard”. The results from these two methods were validated using Area Under Curve (AUC) plots. It is found that hazard zonation map prepared using information value method and analytical hierarchy process methods possess the prediction rate of 78.87% and 75.42%, respectively. Hence, landslide hazard zonation map obtained using information value method is proposed to be more useful for the study area. These final hazard zonation maps can be used by various stakeholders like engineers and administrators for proper maintenance and smooth traffic flow between Chamba and Bharmour cities, which is the only route connecting these tourist places.


Information value Analytical Hierarchy Process Landslide hazard zonation GIS Remote sensing Himalaya 


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The authors are thankful to the public works department of Chamba district Himachal Pradesh for giving required landslide related database and for providing rest house facilities. And also the authors would like to express the appreciation to the editor and reviewers for their valuable comments and suggestions that helped to improve the quality of the paper.


  1. Achour Y, Boumezbeur A, Hadji R, et al. (2017) Landslide susceptibility mapping using analytic hierarchy process and information value methods along a highway road section in Constantine, Algeria. Arabian Journal of Geosciences 10: 194. Scholar
  2. Ahmed F, Rogers JD, Ismail EH (2014) A regional level preliminary landslide susceptibility study of the upper Indus river basin. European Journal of Remote Sensing 47: 343–373. Scholar
  3. Akbar T, Ha S (2011) Landslide hazard zoning along Himalaya Kaghan Valley of Pakistan-by integration of GPS, GIS, and remote sensing technology. Landslides 8 (4): 527–540. Scholar
  4. Aleotti P, Chowdhury R (1999) Landslide hazard assessment: summary review and new perspectives. Bulletin of Engineering Geology and the Environment 58 (1): 21–44. Scholar
  5. Anbalagan R (1992) Landslide hazard evaluation and zonation mapping in mountainous terrain. Engineering geology 32 (4): 269–277. (92)90053-2CrossRefGoogle Scholar
  6. Anbalagan R, Kumar R, Lakshmanan K, et al. (2015) Landslide hazard zonation mapping using frequency ratio and fuzzy logic approach, a case study of Lachung Valley, Sikkim. Geo-Environmental Disasters 2: 6. Stu 2: 81-105CrossRefGoogle Scholar
  7. Andrea F, Andrea G, Giuseppe M (2010) Rock slopes failure susceptibility analysis: from remote sensing measurements to geographic information system raster modules. American Journal of Environmental Sciences 6 (6): 489–494. https://doi. org/10.3844/ajessp.2010.489.494CrossRefGoogle Scholar
  8. Ayalew L, Yamagishi H (2005) The application of GIS based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko mountains, Central Japan. Geomorphology 65 (1/2): 15–31. Scholar
  9. Balsubramani K, Kumaraswamy K (2013) Application of geospatial technology and information value technique in landslide hazard zonation mapping: a case study of Giri Valley, Himachal Pradesh. Disaster Advances 6: 38–47.Google Scholar
  10. Blahut J, VanWesten C, Sterlacchini S (2010) Analysis of landslide inventories for accurate prediction of debris-flow source areas. Geomorphology 119 (1/2): 36–51. https://doi. org/10.1016/j.geomorph.2010.02.017CrossRefGoogle Scholar
  11. Chang K, Liu J (2004) Geo-Imagery Bridging continents. Landslide features interpreted by neural network method using a high resolution satellite image and digital topographical data. Proceedings of 20th ISPRS Congress, Istambul.Google Scholar
  12. Chen W, Chai H, Zhao Z, et al. (2016) Landslide susceptibility mapping based on GIS and support vector machine models for the Qianyang County, China. Environment Earth Sciences 75 (6): 1–13. Scholar
  13. Chung CJF and Fabbri AG (2003) Validation of spatial prediction models for landslide hazard mapping. Natural Hazards 30 (3): 451–472. 0000007172.62651.2bCrossRefGoogle Scholar
  14. Cruden DM, Varnes DJ (1996) Landslide types and processes, special report, Transportation Research Board, National Academy of Sciences 247: 36–75.Google Scholar
  15. Dai FC, Lee CF (2002) Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong. Geomorphology 42 (3): 213–228. (01)00087-3CrossRefGoogle Scholar
  16. Das I, Sahoo S, Van Westen C, et al. (2010) Landslide susceptibility assessment using logistic regression and its comparison with a rock mass classification system, along a road section in the northern Himalayas (India). Geomorphology 114 (4): 627–637. geomorph.2009.09.023CrossRefGoogle Scholar
  17. Deeken A, Thiede RC, Sobel ER, et al. (2011) Exhumational variability within the Himalaya of northwest India. Earth and Planetary Science Letters 305: 103–114. Scholar
  18. Demir G, Aytekin M, Akgun A, et al. (2013) A comparison of landslide susceptibility mapping of the eastern part of the North Anatolian Fault Zone (Turkey) by likelihood-frequency ratio and analytic hierarchy process methods. Natural Hazards 65: 1481–1506. Scholar
  19. 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. Scholar
  20. Fawcett T (2006) An introduction to ROC analysis. Pattern Recognition Letters 27: 861–874. patrec.2005.10.010CrossRefGoogle Scholar
  21. Feizizadeh B, Blaschke T (2012) GIS-multicriteria decision analysis for landslide susceptibility mapping: comparing three methods for the Urmia lake basin, Iran. Natural Hazards 65: 2105–2128. Scholar
  22. Feizizadeh B, Blaschke T, Nazmfar H, et al. (2013) Landslide susceptibility mapping for the Urmia Lake basin, Iran: a multi-criteria evaluation approach using GIS. International Journal of Environmental Research 7 (2): 319–3336.Google Scholar
  23. Frank W, Grasemann B, Guntli P, et al. (1995) Geological map of the Kishtwar-Chamba-Kulu region (NW Himalaya India). Jahrbuch Der Geologischen Bundesanstalt 138 (2): 299–308.Google Scholar
  24. Frattini P, Crosta G, Carrara A (2010) Techniques for evaluating the performance of landslide susceptibility models. Engineering Geology 111: 62–72. enggeo.2009.12.004CrossRefGoogle Scholar
  25. Gomez H, Bradshow R, Mather P (2000) Monitoring the distribution of shallow landslide prone areas using Remote Sensing, DTM and GIS - a case study from the tropical Andes of Venezuela. In: Casanova E (ed) Remote Sensing in 21st century: Economic and Environmental applications. Balkema, Rotterndam. pp 395–401.Google Scholar
  26. Guru B, Veerappan R, Mangminlen T (2016) Landslide susceptibility zonation mapping using frequency ratio and fuzzy gamma operator models in part of NH-39, Manipur, India. Natural Hazards 84: 465–488. Scholar
  27. Guzzetti F (2003) Landslide Hazard Assessment and Risk Evaluation: Limits and Perspectives. In Proceedings of the 4th EGS Plinius Conference held at Mallorca, Spain. University de les IllesBalears, pain. pp 1–4.Google Scholar
  28. Guzzetti F, Carrara A, Cardinali M, et al. (1999) Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology 31 (1): 181–216. Scholar
  29. Guzzetti F, Reichenbach P, Ardizzone M, et al. (2006) Estimating the quality of landslides susceptibility models. Geomorphology 81: 166–184. geomorph.2006.04.007CrossRefGoogle Scholar
  30. Hutchinson JN (1995) Landslide hazard assessment. In: Proc VIInt. Symp on Landslides, Christchurch, Vol. 1. pp 1805–1842Google Scholar
  31. Jaiswal P, Van Westen CJ, Jetten V (2010) Quantitative landslide hazard assessment along a transportation corridor in southern India. Engineering Geology 116: 236–250. Scholar
  32. Kanungo DP, Arora MK, Sarkar S, et al. (2009) Landslide Susceptibility Zonation (LSZ) Mapping - A Review. Journal of South Asia Disaster Studies 2 (1): 81–105.Google Scholar
  33. Kavzoglu T, Sahin EK, Colkesen I (2014) Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression. Landslides 11: 425–439. Scholar
  34. Kayastha P, Dhital MR, DeSmedt F (2013) Application of the analytical hierarchy process (AHP) for landslide susceptibility mapping: a case study from the Tinau watershed, west Nepal. Computers & Geosciences 52: 398–408. Scholar
  35. Kumar R, Anbalagan R (2016) Landslide susceptibility mapping using analytical hierarchy process (AHP) in Tehri Reservoir Rim Region, Uttarakhand. Journal Geological Society of India 87 (3): 271–286. Scholar
  36. Kumar S, Mahajan AK (2001) Seismotectonics of the Kangra region north Himalaya. Tectonophysics 331 (4): 359–371CrossRefGoogle Scholar
  37. Lee S, Min K (2001) Statistical analysis of landslide susceptibility at Yongin, Korea. Environmental Geology 40 (9): 1095–1113.CrossRefGoogle Scholar
  38. Lee S, Hwang J, Park I (2013) Application of data-driven evidential belief functions to landslide susceptibility mapping in Jinbu, Korea. Catena 100: 15–30. Scholar
  39. Leir M, Michell A, Ramsay S (2004) Regional landslide hazard susceptibility mapping for pipelines in British Columbia. Geoengineering for the society and its environment. In: 57th Canadian geotechnical conference and the 5th joint CGS-IAH conference. pp 1–9.Google Scholar
  40. Mondal S, Maiti R (2012) Landslide susceptibility analysis of Shiv-Khola Watershed, Darjiling; a remote sensing and GIS based Analytic Hierarchy Process. Journal of Indian Society of Remote Sensing 3: 483–496.CrossRefGoogle Scholar
  41. Nadim F, Kjekstad O, Peduzzi P, et al. (2006) Global landslide and avalanche hotspots. Landslides 3: 159–173. https://doi. org/10.1007/s10346-006-0036-1CrossRefGoogle Scholar
  42. Nandi A and Shakoor A (2009) A GIS-based landslide susceptibility evaluation using bivariate and multivariate statistical analyses. Engineering Geology 110: 11–20. Scholar
  43. Pandey DD, Singh KP, Sarda VK (2016) GIS based inventory study of landslide hazard zonation in LahaulSpiti Valley between Rohtang to Baralacha La, Himachal Pradesh, India. International Journal of Earth Sciences and Engineering 09: 2847–2854.Google Scholar
  44. Pourghasemi HR, 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 (2): 965–996CrossRefGoogle Scholar
  45. Pradhan B (2010) Application of an advanced fuzzy logic model for landslide susceptibility analysis. International Journal of Computational Intelligence Systems 3 (3): 370–381.CrossRefGoogle Scholar
  46. Pradhan B, Lee S (2010) Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models. Environmental Earth Sciences 60: 1037–1054. https://doi. org/10.1007/s12665-009-0245-8CrossRefGoogle Scholar
  47. Ramakrishnan D, Singh TN, Verma AK, et al. (2013) Soft computing and GIS for landslide susceptibility assessment in Tawaghat area, Kumaon Himalaya, India. Natural Hazards 65: 315–330. Scholar
  48. Ramesh V, Anbazhagan S (2015) Landslide susceptibility assessment along Kohli hills Ghat road section India using frequency ratio, relative effect and fuzzy logic models. Environmental Earth Sciences 73 (12): 8009–8021. https://doi. org/10.1007/s12665-014-3954-6CrossRefGoogle Scholar
  49. Saaty TL (1980) The Analytic Hierarchy Process (New York: McGraw Hill. International, Translated to Russian, Portuguese, and Chinese, Revised editions, Paperback.Google Scholar
  50. Saaty TL (1990) An exposition of the AHP in reply to the paper “remarks on the analytic hierarchy process. Management Science 36 (3): 259–268.CrossRefGoogle Scholar
  51. Saaty TL (1994) Highlights and critical points in the theory and application of the analytic hierarchy process. European Journal of Operational Research 74 (3): 426–447.CrossRefGoogle Scholar
  52. Saaty TL, Vargas LG (2000) Models, Methods, Concepts and Applications of the Analytic Hierarchy Process. Boston: Kluwer Academic Publisher.Google Scholar
  53. Sahana M, Sajjad HJ (2017) Evaluating effectiveness offrequency ratio, fuzzy logic and logistic regression models in assessing landslide susceptibility: a case from Rudraprayag district, India. Journal of Mountain Science 14 (11): 2150. Scholar
  54. Sarkar S, Kanungo D, Mehrotra G (1995) Landslide hazard zonation: a case study of Garhwal Himalaya, India. Mountain Research and Development 15: 301–309.CrossRefGoogle Scholar
  55. Sarkar S, Kanungo DP (2004) An integrated approach for landslide susceptibility mapping using remote sensing and GIS. Photogrammetric Engineering & Remote Sensing 70 (5): 617–625.CrossRefGoogle Scholar
  56. Shahabi H, Hashim M (2015) Landslide susceptibility mapping using GIS-based statistical models and Remote sensing data in tropical environment. Scientific reports 5: 9899. Scholar
  57. Sharma VK, Kumar H, Kumar P (2005) Macro-seismic investigation of Chamba earthquake of 14th April, 2005, Himachal Pradesh. Geol. Surv. India, Unpublished Report, FS2004-2005.Google Scholar
  58. Sujatha ER, Rajamanickam GV, Kumaravel P (2012) Landslide susceptibility analysis using probablistic certainty factor approach: a case study on Tevankarai stream watershed, India. Journal of earth system science 121 (5): 1337–1350. Scholar
  59. Thanh LN, De Smedt F (2012) Application of an analytical hierarchical process approach for landslide susceptibility mapping in a Luoi district, ThuaThien Hue Province, Vietnam. Environmental Earth Sciences 66 (7): 1739–1752. https://doi. org/10.1007/s12665-011-1397-xCrossRefGoogle Scholar
  60. Van Westen CJ, Rengers N, Soeters R (2003) Use of geomorphological information in indirect landslide susceptibility assessment. Natural Hazards 30 (3): 399–419. Scholar
  61. VanWesten CJ (1993) Application of geographic information systems to landslide hazard zonation.ITC Publication, vol. 15. International Institute for Aerospace and Earth Resources Survey, Enschede. p 245.Google Scholar
  62. Vargas LG (1990) An overview of the analytic hierarchy process and its applications. European Journal of Operational Research 1 (48): 2–8.CrossRefGoogle Scholar
  63. Varnes DJ (1984) Landslide Hazard Zonation: A Review of Principles and Practice. United Nations Educational, Scientific and Cultural Organization. p 63.Google Scholar
  64. Wadia DN (1931) Thesyntaxes of the north-west Himalya-its rocks, tectonics, and orogeny. Records of the Geological Survey of India 65: 189–220.Google Scholar
  65. 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. Scholar
  66. Yan TZ (1988) Recent advances of quantitative prognoses of landslide in China. In: Proceedings of the fifth international symposium on landslides, Lausanne, Switzerland. Vol. 2. pp 1263–1268.Google Scholar
  67. Yin KL, Yan TZ (1988) Statistical prediction model for slope instability of metamorphosed rocks. In: Bonnard C (ed.) Proc., Fifth International Symposium in Landslides, Lausanne, Vol. 2. A.A. Balkema, Rotterdam. pp 1269–127Google Scholar

Copyright information

© Science Press, Institute of Mountain Hazards and Environment, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Civil Engineering DepartmentNational Institute of TechnologyHamirpurIndia

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