Advertisement

GIS-based comparative study of information value and frequency ratio method for landslide hazard zonation in a part of mid-Himalaya in Himachal Pradesh

  • Akhilesh KumarEmail author
  • Ravi Kumar Sharma
  • Vijay Kumar Bansal
Technical Paper

Abstract

The road network of a developing country plays a vital role in its overall development. It is therefore important to ascertain landslide hazard assessment along a road network. Landslide hazard zonation focuses on preparing landslide hazard map by considering major instability factors causing the landslides. The present study deals with geographical information systems-based landslide hazard zonation of the study area. The study area comes under the mid-Himalayan region of Himachal Pradesh, India. The slope failures create major havoc every year due to high frequency of landslides along the road. Eight major landslide-causing factors have been identified for the study area, which includes slope, relative relief, curvature, aspect, drainage density, lithology, lineament density, and land use/land cover. Corresponding to each landslide causative factor, a layer has been prepared and landslide hazard zonation maps of the study area were evolved by using frequency ratio and information value methods. Approximately, 75% of the total landslides which occurred in the study area were used for training purpose, and remaining 25% were used for the validation of results using the area under curve technique. The developed landslide hazard zonation maps show satisfactory agreement with the landslide inventory of the study area. The success rate curve obtained for frequency ratio method is 77.18%, and for information value method, it is 74.76%. The validation of result illustrates that frequency ratio method is more accurate as compared to information value method for the study area. The present study shows that geographical information system provides a better environment for modelling of the statistical techniques, that is, frequency ratio and information value method in the present study.

Keywords

Landslide hazard Geographical information system Frequency ratio method Information value method Area under curve 

References

  1. 1.
    Fernández T, Irigaray C, El Hamdouni R, Chacón J (2003) Methodology for landslide susceptibility mapping by means of a GIS. Application to the Contraviesa area (Granada, Spain). Nat Hazards 30(3):297–308Google Scholar
  2. 2.
    Süzen ML, Doyuran V (2004) Data driven bivariate landslide susceptibility assessment using geographical information systems: a method and application to Asarsuyu catchment. Turkey. Eng. Geol. 71:303–321Google Scholar
  3. 3.
    Zhu AX, Wang R, Qiao J, Qin CZ, Chen Y, Liu J, Du F, Lin Y, Zhu T (2014) An expert knowledge-based approach to landslide susceptibility mapping using GIS and fuzzy logic. Geomorphology 214:128–138Google Scholar
  4. 4.
    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. Geomorphology 31:181–216Google Scholar
  5. 5.
    Thiery Y, Malet JP, Sterlacchini S, Puissant A, Maquaire O (2007) Landslide susceptibility assessment by bivariate methods at large scales: application to a complex mountainous environment. Geomorphology 92:38–59Google Scholar
  6. 6.
    Nefeslioglu HA, Gokceoglu C, Sonmez H (2008) An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps. Eng Geol 97(3–4):171–191Google Scholar
  7. 7.
    Chauhan S, Sharma M, Arora MK (2010) Landslide susceptibility zonation of the Chamoli region, Garhwal Himalayas, using logistic regression model. Landslides 7(4):411–423Google Scholar
  8. 8.
    Bai S, Lü G, Wang J, Zhou P, Ding L (2011) GIS-based rare events logistic regression for landslide-susceptibility mapping of Lianyungang. China. Environmental Earth Sciences 62(1):139–149Google Scholar
  9. 9.
    Akgun A (2012) A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: a case study at İzmir, Turkey. Landslides 9(1):93–106Google Scholar
  10. 10.
    Carrara A, Cardinali M, Detti R, Guzzetti F, Pasqui V, Reichenbach P (1991) GIS techniques and statistical models in evaluating landslide hazard. Earth Surf Proc Land 16(5):427–445Google Scholar
  11. 11.
    Baeza C, Corominas J (2001) Assessment of shallow landslide susceptibility by means of multivariate statistical techniques. Earth Surf Process Landf J Br Geomorphol Res Gr 26(12):1251–1263Google Scholar
  12. 12.
    Van Den Eeckhaut M, Reichenbach P, Guzzetti F, Rossi M, Poesen J (2009) Combined landslide inventory and susceptibility assessment based on different mapping units: an example from the Flemish Ardennes, Belgium. Nat Hazards Earth Syst Sci 9(2):507–521Google Scholar
  13. 13.
    Akgun A, Dag S, Bulut F (2008) Landslide susceptibility mapping for a landslide-prone area (Findikli, NE of Turkey) by likelihood-frequency ratio and weighted linear combination models. Environ Geol 54(6):1127–1143Google Scholar
  14. 14.
    Choi J, Oh HJ, Lee HJ, Lee C, Lee S (2012) Combining landslide susceptibility maps obtained from frequency ratio, logistic regression, and artificial neural network models using ASTER images and GIS. Eng Geol 124:12–23Google Scholar
  15. 15.
    Demir G, Aytekin M, Akgün A, Ikizler SB, Tatar O (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. Nat Hazards 65(3):1481–1506Google Scholar
  16. 16.
    Gomez H, Kavzoglu T (2005) Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River Basin, Venezuela. Eng Geol 78(1–2):11–27Google Scholar
  17. 17.
    Gorsevski PV, Brown MK, Panter K, Onasch CM, Simic A, Snyder J (2016) Landslide detection and susceptibility mapping using LiDAR and an artificial neural network approach: a case study in the Cuyahoga Valley National Park, Ohio. Landslides 13(3):467–484Google Scholar
  18. 18.
    Neaupane KM, Piantanakulchai M (2006) Analytic network process model for landslide hazard zonation. Eng Geol 85(3–4):281–294Google Scholar
  19. 19.
    Ayalew L, Yamagishi H, 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(1):73–81Google Scholar
  20. 20.
    Komac M (2006) A landslide susceptibility model using the analytical hierarchy process method and multivariate statistics in perialpine Slovenia. Geomorphology 74(1–4):17–28Google Scholar
  21. 21.
    Zhang G, Cai Y, Zheng Z, Zhen J, Liu Y, Huang K (2016) Integration of the statistical index method and the analytic hierarchy process technique for the assessment of landslide susceptibility in Huizhou, China. CATENA 142:233–244Google Scholar
  22. 22.
    Kumar A, Krishna AP (2018) Assessment of groundwater potential zones in coal mining impacted hard-rock terrain of India by integrating geospatial and analytic hierarchy process (AHP) approach. Geocarto Int 33(2):105–129Google Scholar
  23. 23.
    Sharma RK, Mehta BS (2012) Macro-zonation of landslide susceptibility in Garamaura-Swarghat-Gambhar section of national highway 21, Bilaspur District, Himachal Pradesh (India). Nat Hazards 60(2):671–688Google Scholar
  24. 24.
    Jaafari A, Rezaeian J, Omrani MSO (2017) Spatial prediction of slope failures in support of forestry operations safety. Croat J For Eng J Theory Appl For Eng 38(1):107–118Google Scholar
  25. 25.
    Sharma RK, Mehta BS, Jamwal CS (2013) Cut slope stability evaluation of NH-21 along Nalayan-Gambhrola section, Bilaspur district, Himachal Pradesh, India. Nat Hazards 66(2):249–270Google Scholar
  26. 26.
    Oh HJ, Lee S, Chotikasathien W, Kim CH, Kwon JH (2009) Predictive landslide susceptibility mapping using spatial information in the Pechabun area of Thailand. Environ Geol 57(3):641Google Scholar
  27. 27.
    Dahal RK, Bhandary NP, Hasegawa S, Yatabe R (2014) Topo-stress based probabilistic model for shallow landslide susceptibility zonation in the Nepal Himalaya. Environ Earth Sci 71(9):3879–3892Google Scholar
  28. 28.
    Van Westen CJ, Castellanos E, Kuriakose SL (2008) Spatial data for landslide susceptibility, hazard, and vulnerability assessment: an overview. Eng Geol 102(3–4):112–131Google Scholar
  29. 29.
    Cevik E, Topal T (2003) GIS-based landslide susceptibility mapping for a problematic segment of the natural gas pipeline, Hendek (Turkey). Environ Geol 44(8):949–962Google Scholar
  30. 30.
    Huabin W, Gangjun L, Weiya X, Gonghui W (2005) GIS-based landslide hazard assessment: an overview. Prog Phys Geogr 29(4):548–567Google Scholar
  31. 31.
    Mondal S, Maiti R (2012) Landslide susceptibility analysis of Shiv-Khola watershed, Darjiling: a remote sensing & GIS based analytical hierarchy process (AHP). J Indian Soc Remote Sens 40(3):483–496Google Scholar
  32. 32.
    Yilmaz I (2010) Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine. Environ Earth Sci 61(4):821–836Google Scholar
  33. 33.
    Tian Y, XiaO C, Liu Y, Wu L (2008) Effects of raster resolution on landslide susceptibility mapping: a case study of Shenzhen. Sci China Ser E Technol Sci 51(2):188–198Google Scholar
  34. 34.
    Lee S, Choi J, Woo I (2004) The effect of spatial resolution on the accuracy of landslide susceptibility mapping: a case study in Boun, Korea. Geosci J 8(1):51Google Scholar
  35. 35.
    Nandi A, Shakoor A (2010) A GIS-based landslide susceptibility evaluation using bivariate and multivariate statistical analyses. Eng Geol 110(1–2):11–20Google Scholar
  36. 36.
    Elmahdy SI, Mohamed MM (2014) Groundwater potential modelling using remote sensing and GIS: a case study of the Al Dhaid area, United Arab Emirates. Geocarto Int 29(4):433–450Google Scholar
  37. 37.
    Chen W, Li W, Chai H, Hou E, Li X, Ding X (2016) GIS-based landslide susceptibility mapping using analytical hierarchy process (AHP) and certainty factor (CF) models for the Baozhong region of Baoji City, China. Environ Earth Sc 75(1):63Google Scholar
  38. 38.
    Yin KL, Yan TZ (1988) Statistical prediction model for slope instability of metamorphosed rocks. In Proceedings of the 5th international symposium on landslides, Lausanne, vol 2, pp 1269 − 1272. A.A. Balkema, RotterdamGoogle Scholar
  39. 39.
    Van Westen CJ (1993) Application of geographic information systems to landslide hazard zonation ITC Publication, vol 15, pp 245. International Institute for Aerospace and Earth Resources Survey, EnschedeGoogle Scholar
  40. 40.
    Champatiray P (2000) Perationalization of cost-effective methodology for landslide hazard zonation using RS and GIS: IIRS initiative. In: Roy P, Van Westen C, Jha V, Lakhera R (eds) Natural disasters and their mitigation; remote sensing and geographical information system perspectives. Indian Institute of Remote Sensing, Dehradun, India, pp 95−101Google Scholar
  41. 41.
    Lee S, Talib JA (2005) Probabilistic landslide susceptibility and factor effect analysis. Environ Geol 47(7):982–990Google Scholar
  42. 42.
    Pradhan B, Lee S (2010) Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling. Environ Model Softw 25(6):747–759Google Scholar
  43. 43.
    Karim S, Jalileddin S, Ali MT (2011) Zoning landslide by use of frequency ratio method (case study: Deylaman Region). Middle-East J Sci Res 9(5):578–583Google Scholar
  44. 44.
    Pradhan B, Youssef AM (2010) Manifestation of remote sensing data and GIS on landslide hazard analysis using spatial-based statistical models. Arab J Geosci 3(3):319–326Google Scholar
  45. 45.
    Lee S, Sambath T (2006) Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models. Environ Geol 50(6):847–855Google Scholar
  46. 46.
    Sidle RC, Ochiai H (2006) Landslides: processes, prediction, and land use, vol 18. American Geophysical Union, Washington.  https://doi.org/10.1029/WM011 CrossRefGoogle Scholar
  47. 47.
    Magliulo P, Di Lisio A, Russo F, Zelano A (2008) Geomorphology and landslide susceptibility assessment using GIS and bivariate statistics: a case study in southern Italy. Nat Hazards 47(3):411–435Google Scholar
  48. 48.
    Kayastha P, Dhital MR, De Smedt F (2013) Application of the analytical hierarchy process (AHP) for landslide susceptibility mapping: a case study from the Tinau watershed, west Nepal. Comput Geosci 52:398–408Google Scholar
  49. 49.
    Banerjee P, Ghose MK, Pradhan R (2018) Analytic hierarchy process and information value method-based landslide susceptibility mapping and vehicle vulnerability assessment along a highway in Sikkim Himalaya. Arab J Geosci 11(7):139Google Scholar
  50. 50.
    Dekker LW (1994) How water moves in a water-repellent sandy soil: 1. Potential and actual water repellency. Water Resour Res 30(9):2507–2517Google Scholar
  51. 51.
    Goetz JN, Brenning A, Petschko H, Leopold P (2015) Evaluating machine learning and statistical prediction techniques for landslide susceptibility modelling. Comput Geosci 81:1–11Google Scholar
  52. 52.
    Basharat M, Ali A, Jadoon IA, Rohn J (2016) Using PCA in evaluating event-controlling attributes of landsliding in the 2005 Kashmir earthquake region, NW Himalayas, Pakistan. Nat Hazards 81(3):1999–2017Google Scholar
  53. 53.
    Pham BT, Bui DT, Pourghasemi HR, Indra P, Dholakia MB (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 128(1–2):255–273Google Scholar
  54. 54.
    Ercanoglu M, Gokceoglu C (2002) Assessment of landslide susceptibility for a landslide-prone area (north of Yenice, NW Turkey) by fuzzy approach. Environ Geol 41(6):720–730Google Scholar
  55. 55.
    Nefeslioglu HA, Duman TY, Durmaz S (2008) Landslide susceptibility mapping for a part of tectonic Kelkit Valley (Eastern Black Sea region of Turkey). Geomorphology 94(3–4):401–418Google Scholar
  56. 56.
    Troch P, Van Loon E, Hilberts A (2002) Analytical solutions to a hillslope-storage kinematic wave equation for subsurface flow. Adv Water Resour 25(6):637–649Google Scholar
  57. 57.
    Dikshit A, Satyam DN (2018) Estimation of rainfall thresholds for landslide occurrences in Kalimpong, India. Innov Infrast Solut 3(1):24Google Scholar
  58. 58.
    Pourghasemi HR, Kerle N (2016) Random forests and evidential belief function-based landslide susceptibility assessment in Western Mazandaran Province, Iran. Environ Earth Sci 75(3):185Google Scholar
  59. 59.
    Lee S, Choi J, Min K (2004) Probabilistic landslide hazard mapping using GIS and remote sensing data at Boun, Korea. Int J Remote Sens 25(11):2037–2052Google Scholar
  60. 60.
    Hazra PB (1999) Land forms and land use in the upland India. Naya Prokash, Calcutta, p 169Google Scholar
  61. 61.
    Chandel VB, Brar KK, Chauhan Y (2011) RS & GIS based landslide hazard zonation of mountainous terrains a study from Middle Himalayan Kullu District, Himachal Pradesh, India. Int J Geomatics Geosci 2(1):121Google Scholar
  62. 62.
    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(3):425–439Google Scholar
  63. 63.
    Strahler AN (1964) Part II. Quantitative geomorphology of drainage basins and channel networks. Handbook of applied hydrology. McGraw-Hill, New York, pp 4–39Google Scholar
  64. 64.
    Greenbaum D, Tutton M, Bowker MR, Browne TJ, Buleka J, Greally KB, O’Connor EA (1995) Rapid methods of landslide hazard mapping: Papua New Guinea case study. Br Geol Surv 27:1–112Google Scholar
  65. 65.
    Pham BT, Prakash I, Bui DT (2018) Spatial prediction of landslides using a hybrid machine learning approach based on Random Subspace and Classification and Regression Trees. Geomorphology 303:256–270Google Scholar
  66. 66.
    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):15–31Google Scholar
  67. 67.
    Bougdal R, Belhai D, Antoine P (2006) Geology of the city of Constantine and its surroundings. Bull Serv Géol Alger 18:3–23Google Scholar
  68. 68.
    Yalcin A, Bulut F (2007) Landslide susceptibility mapping using GIS and digital photogrammetric techniques: a case study from Ardesen (NE-Turkey). Nat Hazards 41(1):201–226Google Scholar
  69. 69.
    Wu Y, Li W, Liu P, Bai H, Wang Q, He J, Sun S (2016) Application of analytic hierarchy process model for landslide susceptibility mapping in the Gangu County, Gansu Province, China. Environ Earth Sci 75(5):422Google Scholar
  70. 70.
    Mohammady M, Pourghasemi HR, Pradhan B (2012) Landslide susceptibility mapping at Golestan Province, Iran: a comparison between frequency ratio, Dempster-Shafer, and weights-of-evidence models. J Asian Earth Sci 61:221–236Google Scholar
  71. 71.
    Pourghasemi HR, Moradi HR, Aghda SF (2013) Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances. Nat Hazards 69(1):749–779Google Scholar
  72. 72.
    Bourenane H, Bouhadad Y, Guettouche MS, Braham M (2015) GIS-based landslide susceptibility zonation using bivariate statistical and expert approaches in the city of Constantine (Northeast Algeria). Bull Eng Geol Environ 74(2):337–355Google Scholar
  73. 73.
    Lee S, Ryu JH, Kim IS (2007) Landslide susceptibility analysis and its verification using likelihood ratio, logistic regression, and artificial neural network models: case study of Youngin, Korea. Landslides 4(4):327–338Google Scholar
  74. 74.
    Pourghasemi HR, Pradhan B, Gokceoglu C (2012) Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Nat Hazards 63(2):965–996Google Scholar
  75. 75.
    Zweig MH, Campbell G (1993) Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin Chem 39(4):561–577Google Scholar
  76. 76.
    Kumar R, Anbalagan R (2016) Landslide susceptibility mapping using analytical hierarchy process (AHP) in Tehri reservoir rim region, Uttarakhand. J Geol Soc India 87(3):271–286Google Scholar
  77. 77.
    Gupta RP, Kanungo DP, Arora MK, Sarkar S (2008) Approaches for comparative evaluation of raster GIS-based landslide susceptibility zonation maps. Int J Appl Earth Obs Geoinf 10(3):330–341Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Civil EngineeringNational Institute of TechnologyHamirpurIndia

Personalised recommendations