Advertisement

Probabilistic Approaches and Landslide Susceptibility

  • Sujit MandalEmail author
  • Subrata Mondal
Chapter
Part of the Environmental Science and Engineering book series (ESE)

Abstract

The present study is associated with the implication of weight of evidence model and certainty factor model to prepare landslide susceptibility maps of the Balason river basin of Darjeeling Himalaya using data layers of elevation, slope, aspect, curvature, geology, geomorphology, soil, distance to lineament, lineament density, drainage density, distance to drainage, stream power index (SPI), topographic wetness index (TWI), land use and land cover (LULC) and NDVU in ARC GIS 10.1. The developed landslide susceptibility map was classified in five i.e. very low, low, moderate, high and very high landslide susceptibility. The prepared landslide susceptibility maps were also validated using ROC curve which stated that certainty factor mode is best suited for developing landslide susceptibility zonation map of the Balason river basin of Darjeeling Himalaya.

References

  1. 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 38:23–34CrossRefGoogle Scholar
  2. Basu SR, Sarkar S (1987) Ecosystem vis-a vis landslide: a case study in Darjeeling Himalaya, proce. Impact of development on environment. Geog Soc India 2, 45–53. Unpublished GSI report, FS 1981–82, 1982–83Google Scholar
  3. Biswas SS, Pal R (2015) Causes of Landslides in Darjeeling Himalayas during June-July, 2015. J Geogr Nat Disasters 2016(6):2.  https://doi.org/10.4172/2167-0587.1000173CrossRefGoogle Scholar
  4. Binaghi E, Luzi L, Madella P (1998) Slope instability zonation: a comparison between certainty factor and fuzzy Dempster–Shafer approaches. Nat Hazards 17:77–97Google Scholar
  5. Bonham-Carter GF (1994) Geographic information systems for geoscientists: modelling with GIS. In: Computer methods in the geosciences, vol 13. Pergamon Press, Oxford, p 398Google Scholar
  6. Bonham-Carter GF, Agterberg FP, Wright DF (1988) Integration of geological datasets for gold exploration in Nova Scotia. Photogramm Eng Remote Sens 54:1585–1592Google Scholar
  7. Bonham-Carter, G.F., Agterberg, F.P. and Wright, D.F., (1989) Weights of evidence modelling: a new approach to mapping mineral potential. In: Statistical applications in the earth science. Geological survey of Canada, Paper 89–9, 171–183Google Scholar
  8. 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):337–355CrossRefGoogle Scholar
  9. Bui DT, Pradhan B, Lofman O, Revhaug I, Dick OB (2012) Spatial prediction of landslide hazards in Hoa Binh province (Vietnam): a comparative assessment of the efficacy of evidential belief functions and fuzzy logic models. Catena 96:28–40CrossRefGoogle Scholar
  10. Chen W, Li W, Hou E, Zhao Z, Deng N, Bai H, Wang D (2014) Landslide susceptibility mapping based on GIS and information value model for the Chencang District of Baoji, China, 2014. Arab J Geosci.  https://doi.org/10.1007/s12517-014-1369-zCrossRefGoogle Scholar
  11. Chung CF, Fabbri AG (1993) The representation of geosciences information for data integration. Nonrenew Resour 2(2):122–139 CrossRefGoogle Scholar
  12. Corsini A, Cervi F, Daehne A, Ronchetti F (2009) Coupling geomorphic field observation and LIDAR derivatives to map complex landslides. In: Malet JP, Remaître A, Bogaard T (eds) Landslides processes—from geomorphologic mapping to dynamic modeling, proceedings of the landslide processes conference, 6–7 February 2009, StrasbourgGoogle Scholar
  13. Dahal RK, Hasegawa S, Nonomura A, Yamanaka M, Dhakal S, Paudyal P (2008a) Predictive modelling of rainfall-induced landslide hazard in the Lesser Himalaya of Nepal based on weights-of-evidence. Geomorphology 102:496–510CrossRefGoogle Scholar
  14. Dahal RK, Hasegawa S, Nonomura A, Yamanaka M, Masuda T, Nishino K (2008b) GIS-based weights-of evidence modelling of rainfall-induced landslides in small catchments for landslide susceptibility mapping. Environ Geol 54(2):314–324CrossRefGoogle Scholar
  15. Davis JC (2002) Statistics and data analysis in geology, 3rd ed. Wiley, 638 pp. ERDAS, 1997. ERDAS field guide, 5th edn. ERDAS, Inc., Atlanta, Georgia, USA, 672 ppGoogle Scholar
  16. Ghosh S, Carranza EJM, van Westen CJ, Jetten VG, Bhattacharya DN (2011) Selecting and weighting spatial predictors for empirical modeling of landslide susceptibility in the Darjeeling Himalayas (India). Geomorphology 131(1):35–56CrossRefGoogle Scholar
  17. Guettouche MS (2013) Modeling and risk assessment of landslides using fuzzy logic: application on the slopes of the Algerian Tell (Algeria). Arab J Geosci 6:3163–3173CrossRefGoogle Scholar
  18. Guha-Sapir D, Below R, Hoyois PH (2018) EM-DAT: international disaster database. http://www.emdat.be, Université Catholique de Louvain, Brussels, Belgium, last access 19 Feb 2018
  19. Gupta RP, Joshi BC (1990) Landslide hazard zonation using the GIS approach—a case study from the Ramganga Catchment, Himalayas. Eng Geol 28:119–131CrossRefGoogle Scholar
  20. Hong H, Pradhan B, Xu C, Bui DT (2015) Spatial prediction of landslide hazard at the Yihuang area (China) using two-class kernel logistic regression, alternating decision tree and support vector machines. CATENA 133:266–281CrossRefGoogle Scholar
  21. Kanungo DP, Arora MK, Sarkar S, Gupta RP (2006) A comparative study of conventional, ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility Zonation in Darjeeling Himalayas. Eng Geol 85:347–366CrossRefGoogle Scholar
  22. Kanungo DP, Sarkar S, Sharma S (2011) Combining Neural Network with fuzzy, certainty factor and likelihood ratio concepts for spatial prediction of landslide. Nat Hazards 59(3):1491–1512CrossRefGoogle Scholar
  23. Lee S, Choi J (2004) Landslide susceptibility mapping using GIS and the weight-of-evidence model. Int J Geogr Inf Sci 18(8):789–814.  https://doi.org/10.1080/13658810410001702003CrossRefGoogle Scholar
  24. Lee S, Choi J, Min K (2002) Landslide susceptibility analysis and verification using the Bayesian probability model. Environ Geol 43(1–2):120–131CrossRefGoogle Scholar
  25. Lee S, Pradhan B (2007) Landslide hazard mapping at Selangor Malaysia using frequency ratio and logistic regression models. Landslides 4:33–41CrossRefGoogle Scholar
  26. Liu M, Chen X, Yang S (2014) Collapse landslide and mudslide hazard zonation. In: Landslide science for a safer geoenvironment. Springer International Publishing, pp 457–462Google Scholar
  27. Mandal S, Maiti R (2014) Role of lithological composition and lineaments in landsliding: a case study of Shivkhola watershed. Darjeeling Himal Int J Geol Earth Environ Sci 4(1):126–132Google Scholar
  28. Mathew J, Jha VK, Rawat GS (2007) Weights of evidence modelling for landslide hazard zonation mapping in part of Bhagirathi valley, Uttarakhand. Curr Sci 92(5):628–638Google Scholar
  29. 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 61:221–236CrossRefGoogle Scholar
  30. Naghibi SA, Pourghasemi HR, Dixon B (2016) GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran. Environ Monit Assess 188(1):1–27CrossRefGoogle Scholar
  31. Neuhäuser B, Terhorst B (2007) Landslide susceptibility assessment using “weights-of-evidence” applied to a study area at the Jurassic escarpment (SW-Germany). Geomorphology, 86(1-2):12– 24.  https://doi.org/10.1016/j.geomorph.2006.08.002CrossRefGoogle Scholar
  32. Ozdemir A, 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 64:180–197CrossRefGoogle Scholar
  33. Park S, Choi C, Kim B, 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 68:1443–1464CrossRefGoogle Scholar
  34. Peng L, Niu R, Huang B, Wu X, Zhao Y, Ye R (2014) Landslide susceptibility mapping based on rough set theory and support vector machines: a case of the Three Gorges area, China. Geomorphology 204:287–301CrossRefGoogle Scholar
  35. Pham BT, Tien Bui D, Pham HV (2016) Spatial prediction of rainfall induced landslides using Bayesian Network at Luc Yen District, Yen Bai Province (Viet Nam). In: International conference on environmental issues in mining and natural resources development (EMNR 2016), Hanoi University of mining and geology (HUMG), Viet Nam, pp 1–10Google Scholar
  36. Poli S, Sterlacchini S (2007) Landslide representation strategies in susceptibility studies using weights of-evidence modeling technique. Nat Resour Res 16:121–134CrossRefGoogle Scholar
  37. Pourghasemi HR, Pradhan B, Gokceoglu C, Mohammadi M, Moradi HR (2013a) Application of weights-of-evidence and certainty factor models and their comparison in landslide susceptibility mapping at Haraz watershed, Iran. Arabian J Geosci 6(7):2351–2365CrossRefGoogle Scholar
  38. Pourghasemi HR, Moradi HR, Aghda SF (2013b) Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process and statistical index models and assessment of their performances. Nat Hazards 69(1):749–779CrossRefGoogle Scholar
  39. Pourghasemi HR, Rahmati O (2017) Prediction of the landslide susceptibility: which algorithm, which precision?Google Scholar
  40. Pradhan AMS, Kim YT (2014) Relative effect method of landslide susceptibility zonation in weathered granite soil: a case study in Deokjeok-ri Creek, South Korea. Nat Hazards 72(2):1189–1217CrossRefGoogle Scholar
  41. 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):747–759CrossRefGoogle Scholar
  42. Pradhan B (2013) A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Comput Geosci 51:350–365CrossRefGoogle Scholar
  43. Regmi AD, Devkota KC, Yoshida K, Pradhan B, Pourghasemi HR, Kumamoto T, Akgun A (2014) 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 7:725–742CrossRefGoogle Scholar
  44. Rezaei Moghaddam MH, Khayyam M, Ahmadi M, Farajzadeh M (2007) Mapping susceptibility landslide by using the weight–of-evidence model: a case study in Merek valley. Iran. J Appl Sci 7(22):3342–3355CrossRefGoogle Scholar
  45. Sarkar S et al (2010) Geo-hazards in sub Himalayan North Bengal. Department of Geography & Applied Geography, University of North Bengal, West Bengal, p 2010Google Scholar
  46. Sengupta CK (1995) Detailed study of geofactors in selected hazard prone stretches along the surface communication routes in parts of Darjeeling Himalaya. Unpublished GSI Report, FS 1993–94 & 1994–95Google Scholar
  47. Sharma LP, Nilanchal P, Ghose MK, Debnath P (2013) Synergistic application of fuzzy logic and geoinformatics for landslide vulnerability zonation—a case study in Sikkim Himalayas, India. Appl Geomat 5:271–284CrossRefGoogle Scholar
  48. Spiegelhater D, Knill-Jones RP (1984) Statistical and knowledge approaches to clinical decision-support systems, with an application in gastroenterology. J R Stat Soc 147:35–77Google Scholar
  49. Starkel L, Basu SR (2000) Landslides and floods in the Darjiling Himalayas. New Delhi, 1–168: Indian Science AcademyGoogle Scholar
  50. Sujatha ER, Kumaravel P, Rajamanickam GV (2014) Assessing landslide susceptibility using Bayesian probability-based weight of evidence model. Bull Eng Geol Environ 73:147.  https://doi.org/10.1007/s10064-013-0537-9CrossRefGoogle Scholar
  51. Sujatha ER, Rajamanikam GV, Kumaravel P (2012) Landslide susceptibility analysis using probabilistic certainty factor approach: a case study on Tevankarai stream watershed, India. J Earth Syst Sci 121(5):1337–1350CrossRefGoogle Scholar
  52. 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 92:38–59CrossRefGoogle Scholar
  53. Thiery Y, Sterlacchini S, Malet JP, Puissant A, Remaître A, Maquaire O (2004) Strategy to reduce subjectivity in landslide susceptibility zonation by GIS in complex mountainous environments. In: Toppen F, Prastacos P (eds) Proceedings of AGILE 2004: 7th AGILE conference on geographic information science. 29th Apr–1st May 2004, Heraklion, Greece, pp 623–634Google Scholar
  54. Torkashvand AM, Irani A, Sorur J (2014) The preparation of landslide map by Landslide Numerical Risk Factor (LNRF) model and Geographic Information System (GIS). Egypt J Remote Sens Space Sci 17:159–170CrossRefGoogle Scholar
  55. Tsangaratos P, Benardos A (2014) Estimating landslide susceptibility through an artificial neural network classifier. Nat Hazards 74(3):1–28CrossRefGoogle Scholar
  56. van Westen CJ (1997) Statistical landslide hazard analysis. In: Application guide, ILWIS 2.1 for Windows. ITC, Enschede, The Netherlands, pp 73–84Google Scholar
  57. van Westen CJ, Rengers N, Soeters R (2003) Use of geomorphological information in indirect landslide susceptibility assessment. Nat Hazards 30:399–419CrossRefGoogle Scholar
  58. van Westen CJ, van Asch TWJ, Soeters R (2006) Landslide hazard and risk zonation: why is it still so difficult? Bull Eng Geol Environ 65(5):167–184CrossRefGoogle Scholar
  59. Venables WN, Ripley BD (2002) Modern applied statistics with S, 4th edn. Springer, Berlin, Germany, p 495CrossRefGoogle Scholar
  60. Wang Q, Li W, Chen W, Bai H (2015) GIS-based assessment of landslide susceptibility using certainty factor and index of entropy models for the Qianyang County of Baoji city, China. J Earth Syst Sci 124(7):1399–1415CrossRefGoogle Scholar
  61. Yalcin A, Reis S, Aydinoglu AC, 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):274–287CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of GeographyDiamond Harbour Women’s UniversityDiamond HarbourIndia
  2. 2.Bajitpur High SchoolGangarampurIndia

Personalised recommendations