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Prediction of Landslide Susceptibility Using Bivariate Models

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

Abstract

The present study is dealt with the application of Information value model (IVM), landslide nominal risk factor mode (LNRFM), fuzzy logic approach (FLA) and statistical index model (SIM) and the preparation of landslide susceptibility maps of the Balason river basin of Darjeeling Himalaya using various geomorphic, hydrologic, and tectonic attributes such as 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. All the landslide conditioning factors were being processed in GIS platform. The prepared landslide susceptibility maps were also validated using ROC curve which stated that fuzzy logic approach is best suited for developing landslide susceptibility zonation map of the Balason river basin of Darjeeling Himalaya.

Keywords

Bivariate models Landslide susceptibility ROC curve Model validation Landslide susceptibility Balason river basin 

References

  1. Akbar T, Ha S (2011) Landslide hazard zoning along Himalayan Kaghan Valley of Pakistan—by integration of GPS, GIS, and remote sensing technology. Landslide 8(4):527–540.  https://doi.org/10.1007/s10346-011-0260-1CrossRefGoogle Scholar
  2. Akgun A, Sezer EA, Nefeslioglu HA, Gokceoglu C, Pradhan B (2012) An 277 easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm. Comput Geosci 38:23–34CrossRefGoogle Scholar
  3. 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 Adv 6:38–47Google Scholar
  4. 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
  5. 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. Arab J Geosci.  https://doi.org/10.1007/s12517-014-1369-zCrossRefGoogle Scholar
  6. Daneshvar MRM (2014) Landslide susceptibility zonation using analytical hierarchy process and GIS for the Bojnurd region, northeast of Iran. Landslides 11(6):1079–1091CrossRefGoogle Scholar
  7. Davis JC (2002) Statistics and data analysis in geology, 3rd edn. Wiley, p 638; ERDAS (1997) ERDAS field guide, 5th edn. ERDAS Inc., Atlanta, Georgia, USA, p 672Google Scholar
  8. Dou J, Bui DT, Yunus AP, Jia K, Song X, Revhaug I, Xia H, Zhu Z (2015) Optimization of causative factors for landslide susceptibility evaluation using remote sensing and GIS data in parts of Niigata, Japan. PloS ONE 10(7):e0133262CrossRefGoogle Scholar
  9. Duman TY, Can T, Gokceoglu C, Nefeslioglu HA, Sonmez H (2006) Application of logistic regression for landslide susceptibility zoning of Cekmece area, Istanbul, Turkey. Environ Geol 51(2):241–256CrossRefGoogle Scholar
  10. Fisher RA (1936) The use of multiple measurements in taxonomic problems. Ann Eugenics 7:179–188CrossRefGoogle Scholar
  11. 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
  12. 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
  13. 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
  14. Guzzetti F, Reichenbach P, Cardinali M, Galli M, Ardizzone F (2005) Probabilistic landslide hazard assessment at the basin scale. Geomorphology 72(1):272–299CrossRefGoogle Scholar
  15. Kanungo D, Arrora M, Sarkar S, Gupta R (2009) Landslide susceptibility zonation (LSZ) mapping—a review. J South Asia Disaster Stud 2:81–105 Google Scholar
  16. Lee S, Hwang J, Park I (2013) Application of data-driven evidential belief functions to landslide susceptibility mapping in Jinbu, Korea. Catena 100:15–30CrossRefGoogle Scholar
  17. Ma F, Wang J, Yuan R, Zhao H, Guo J (2013) Application of analytical hierarchy process and least-squares method for landslide susceptibility assessment along the Zhong-Wu natural gas pipeline, China. Landslides 10(4):481–492CrossRefGoogle Scholar
  18. 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
  19. Mondal S, Mandal S (2017a) RS and GIS-based landslide susceptibility mapping of the Balason River basin, Darjeeling Himalaya, using logistic regression (LR) model. Georisk Assess Manag Risk Eng Syst Geohazards 12:1, 29–44.  https://doi.org/10.1080/17499518.2017.1347949Google Scholar
  20. Mondal S, Mandal S (2017b) Application of frequency ratio (FR) model in spatial prediction of landslides in the Balason river basin, Darjeeling Himalaya. Spat Inf Res.  https://doi.org/10.1007/s41324-017-0101-yCrossRefGoogle Scholar
  21. Mandal S, Mandal K (2017) Bivariate statistical index for landslide susceptibility mapping in the Rorachu River basin of Eastern Sikkim Himalaya, India. Spat Inf Res.  https://doi.org/10.1007/s41324-017-0156-9CrossRefGoogle Scholar
  22. Mandal S, Mandal K (2018) Modeling and mapping landslide susceptibility zones using GIS based multivariate binary logistic regression (LR) model in the Rorachu river basin of eastern Sikkim Himalaya, India. Model Earth Syst Environ.  https://doi.org/10.1007/s40808-018-0426-0CrossRefGoogle Scholar
  23. Melo R, Vieira G, Caselli A, Ramos M (2012) Susceptibility modelling of hummocky terrain distribution using the information value method (Deception Island, Antarctic Peninsula). Geomorphology 155, 156:88–95CrossRefGoogle Scholar
  24. Nandi A, Shakoor A (2010) A GIS-based landslide susceptibility evaluation using bivariate and multivariate statistical analyses. Eng Geol 110(1):11–20CrossRefGoogle Scholar
  25. Niu QF, Cheng WM, Lan HX, Liu Y, Xie YW (2011) Susceptibility assessment of secondary geological disaster based on information value methodology for Yushu earthquake region (In Chinese). J Mountain Sci 29:243–249Google Scholar
  26. Pereira S, Zezere J, Bateira C (2012) Assessing predictive capacity and conditional independence of landslide predisposing factors for shallow landslide susceptibility models. Nat Hazards Earth Syst Sci 12:979–988.  https://doi.org/10.5194/nhess129792012
  27. Pourghasemi HR, Mohammady M, Pradhan M (2012) Landslide susceptibility mapping using index of entropy and conditional probability models in GIS: Safarood Basin, Iran. Catena 97:71–84CrossRefGoogle Scholar
  28. Pourghasemi HR, Goli Jirandeh A, Pradhan B, Xu C, Gokceoglu C (2013) Landslide susceptibility mapping using support vector machine and GIS. J Earth Syst Sci 122(2):349–369CrossRefGoogle Scholar
  29. 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
  30. Pradhan B, Oh HJ, Buchroithner M (2010) Weights-of-evidence model applied to landslide susceptibility mapping in a tropical hilly area. Geomat Nat Hazards Risk 1(3):199–223CrossRefGoogle Scholar
  31. Pradhan MSA, Dawadi A, Kim T (2012) Use of different bivariate statistical landslide susceptibility methods: a case study of Kulekhani watershed, Nepal. J Nepal Geol Soc 44(2012):1–12Google Scholar
  32. Saha AK, Gupta RP, Sarkar I, Arora KM, Csaplovics E (2005) An approach for GIS-based statistical landslide susceptibility zonation with a case study in the Himalayas. Landslides 2(1):61–69CrossRefGoogle Scholar
  33. Sharma L, Patel N, Ghosh M, Debnath P (2009) Geographical information system based landslide probabilistic model with trivariate approach—A case study in Sikkim Himalaya. In: Eighteenth united nations regional cartographic conference for Asia and the Pacific, UN Economic and Social Council, BankokGoogle Scholar
  34. Sharma LP, Nilanchal Patel, 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
  35. Sharma LP, Patel N, Ghose MK, Debnath P (2015) Development and application of Shannon’s entropy integrated information value model for landslide susceptibility assessment and zonation in Sikkim Himalaya in India. Nat Hazards 75:1555–1576CrossRefGoogle Scholar
  36. Suh J, Choi Y, Roh TD, Lee HJ, Park HD (2011) National-scale assessment of landslide susceptibility to rank the vulnerability to failure of rock-cut slopes along expressways in Korea. Environ Earth Sci 63(3):619–632CrossRefGoogle Scholar
  37. Tay LT, Lateh H, Hossain MK, Kamil AA (2014) Landslide hazard mapping using a Poisson distribution: a case study in Penang Island, Malaysia. In Landslide science for a safer geoenvironment. Springer International Publishing, pp 521–525Google Scholar
  38. Tien Bui D, Pradhan B, Lofman O, Revhaug I (2012) Landslide susceptibility assessment in Vietnam using support vector machines, decision tree and Naïve Bayes models. Math Probl Eng.  https://doi.org/10.1155/2012/9746382012:26
  39. 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
  40. van Westen CJ (1997) Statistical landslide hazard analysis. In: Application guide, ILWIS 2.1 for Windows. ITC, Enschede, Netherlands, pp 73–84Google Scholar
  41. van Westen CJ, Rengers N, Soeters R (2003) Use of geomorphological information in indirect landslide susceptibility assessment. Nat Hazards 30(3):399–419CrossRefGoogle Scholar
  42. Venables WN, Ripley BD (2002) Modern applied statistics with S, 4th edn. Springer, Berlin, Germany, p 495CrossRefGoogle Scholar
  43. Vijith H, Rejith PG, Madhu G (2009) Using Info Val method and GIS techniques for the spatial modelling of landslides susceptibility in the upper catchment of river Meenachil in Kerala. J Indian Soc Remote Sens 37:241–250CrossRefGoogle Scholar
  44. Xu C (2013) Assessment of earthquake-triggered landslide susceptibility based on expert knowledge and information value methods: a case study of the 20 April 2013 Lushan, China Mw6. 6 earthquake. Disaster Adv, 6(13):119–130Google Scholar
  45. Xu WB, Yu WJ, Jing SC, Zhang GP, Huang JX (2013) Debris flow susceptibility assessment by GIS and information value model in a large-scale region, Sichuan Province (China). Nat Hazards 65:1379–1392CrossRefGoogle Scholar
  46. Yalcin A (2008) GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): comparisons of results and confirmations. Catena 72:1–12CrossRefGoogle Scholar
  47. 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
  48. Yesilnacar EK (2005) The application of computational intelligence to landslide susceptibility mapping in Turkey. Ph.D. thesis, Department of Geomatics, University of Melbourne, p 423Google Scholar
  49. 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(6):1125–1138CrossRefGoogle Scholar
  50. Zadeh LA (1965) Fuzzy sets. Information and control, vol 8/3. Elsevier, pp 338–353Google 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

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