Advertisement

Journal of Mountain Science

, Volume 8, Issue 4, pp 505–517 | Cite as

GIS based landslide susceptibility mapping of Tevankarai Ar sub-watershed, Kodaikkanal, India using binary logistic regression analysis

  • Sujatha E. RamaniEmail author
  • Kumarvel Pitchaimani
  • Victor Rajamanickam Gnanamanickam
Article

Abstract

Landslide susceptibility mapping is the first step in regional hazard management as it helps to understand the spatial distribution of the probability of slope failure in an area. An attempt is made to map the landslide susceptibility in Tevankarai Ar sub-watershed, Kodaikkanal, India using binary logistic regression analysis. Geographic Information System is used to prepare the database of the predictor variables and landslide inventory map, which is used to build the spatial model of landslide susceptibility. The model describes the relationship between the dependent variable (presence and absence of landslide) and the independent variables selected for study (predictor variables) by the best fitting function. A forward stepwise logistic regression model using maximum likelihood estimation is used in the regression analysis. An inventory of 84 landslides and cells within a buffer distance of 10m around the landslide is used as the dependent variable. Relief, slope, aspect, plan curvature, profile curvature, land use, soil, topographic wetness index, proximity to roads and proximity to lineaments are taken as independent variables. The constant and the coefficient of the predictor variable retained by the regression model are used to calculate the probability of slope failure and analyze the effect of each predictor variable on landslide occurrence in the study area. The model shows that the most significant parameter contributing to landslides is slope. The other significant parameters are profile curvature, soil, road, wetness index and relief. The predictive logistic regression model is validated using temporal validation data-set of known landslide locations and shows an accuracy of 85.29 %.

Keywords

Landslide Susceptibility Binary Logistic Regression GIS Kodaikkanal India 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Reference

  1. Aleotti P, Chowdhury R (1999) Landslide hazard assessment: summary, review and new perspectives. Bulletin of Engineering Geology and Environment 58(1): 21–44.CrossRefGoogle Scholar
  2. Anbalagan R (1992) Landslide hazard evaluation and zonation mapping in mountainous terrain. Engineering Geology 32:269–277.CrossRefGoogle Scholar
  3. 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.CrossRefGoogle Scholar
  4. Bai S, Lü G, Wang J, Zhou P, Ding L (2010) GIS-based rare events logistic regression for landslide-susceptibility mapping of Lianyungang, China. Environmental Earth Science doi:10.1007/s12665-010-0509-3.Google Scholar
  5. Binaghi E, Luzi L, Madella P (1998) Slope instability zonation: a comparison between certainty factor and fuzzy Dempster — Shafer approaches. Natural Hazards 17: 77–97.CrossRefGoogle Scholar
  6. Can T, Nefeslioglu HA, Gokceoglu C, Sonmez H, Duman TY (2005) Susceptibility assessments of shallow earth flows triggered by heavy rainfall at three catchments by logistic regression analyses. Geomorphology 72: 250–271.CrossRefGoogle Scholar
  7. Chung CF, Fabbri AG (1999) Probabilistic prediction models for landslide hazard mapping. Photogrammetric Engineering and Remote Sensing 65: 1389–1399.Google Scholar
  8. Dai FC, Lee CF(2002) Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong Geomorphology 42: 213–28.Google Scholar
  9. Duman TY, Can T, Gokceoglu C et al. (2006) Application of logistic regression for landslide susceptibility zoning of Cekmece Area, Istanbul, Turkey. Environmental Geology (2006) 51: 241–256.CrossRefGoogle Scholar
  10. Ercanoglu M (2005) Landslide susceptibility assessment of SE Bartin (West Black Sea region, Turkey) by artificial neural networks. Natural Hazards and Earth System Science 5: 979–992.CrossRefGoogle Scholar
  11. Ercanoglu M, Gokceoglu C (2002) Assessment of landslide susceptibility for a landslide-prone area (north of Yenice, NW Turkye) by fuzzy approach. Environmental Geology 41: 720–730.CrossRefGoogle Scholar
  12. Ermini L, Catani F, Casagli N (2005) Artificial Neural Networks applied to landslide susceptibility assessment. Geomorphology 66: 327–343.CrossRefGoogle Scholar
  13. ER. Sujatha and Rajamanickam V (2011a) Landslide susceptibility mapping of Tevankarai Ar Sub-Watershed, Kodaikkanal Taluk, India, using weighted similar choice fuzzy model Natural Hazards, doi: 10.1007/S11069-011-9763-2.Google Scholar
  14. ER. Sujatha and Rajamanickam V (2011b) Landslide susceptibility analysis using probabilistic frequency ratio model — a geospatial based study. Arabian Journal of Geosciences, doi: 10.1007/s12517-011-0356-x.Google Scholar
  15. Gokceoglu C, Aksoy H (1996) Landslide susceptibility mapping of the slopes in the residual soils of the Mengen region (Turkey) by deterministic stability analysis and image processing techniques. Engineering Geology 44(1–4): 147–61.CrossRefGoogle Scholar
  16. Gokceoglu C, Sonmez H, Nefeslioglu HA, Duman TY, Can T (2005) The March 17, 2005 Kuzulu landslide (Sivas, Turkey) and landslide susceptibility map of its near vicinity. Engineering Geology 81(1): 65–83.CrossRefGoogle Scholar
  17. Gomez H, Kavzoglu T (2005) Assessment of shallow landslide susceptibility using artificial neural network in Jabonosa River basin, Venezuela. Engineering Geology 78: 11–27.CrossRefGoogle Scholar
  18. Guzzetti F, Carrara A, Cardinali M, Reichenbach P (1999) Landslide hazard evaluation: a review of current techniques and their application in a multiscale study, Central Italy. Geomorphology 31: 181–216.CrossRefGoogle Scholar
  19. Lee S (2007a) Application and verification of fuzzy algebraic operators to landslide susceptibility mapping. Environmental Geology 52: 615–623.CrossRefGoogle Scholar
  20. Lee S (2007b) Comparison of landslide susceptibility maps generated through multiple logistic regression for three test areas in Korea. Earth Surf Processes Landforms 32: 2133–2148.CrossRefGoogle Scholar
  21. Lee S, Evangelista DG (2006) Earthquake induced landslide susceptibility mapping using an artificial neural network. Natural Hazards and Earth System Sciences 6: 687–695.CrossRefGoogle Scholar
  22. Lee S, Pradhan B (2007) Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides 4: 33–41.CrossRefGoogle Scholar
  23. Magliulo P, Antonio Di Lisio, Filippo Russo, Antonio Zelano (2008) Geomorphology and landslide susceptibility assessment using GIS and bivariate statistics: a case study in southern Italy. Natural Hazards 47: 411–435.CrossRefGoogle Scholar
  24. Mathew J, Jha VK, Rawat GS (2009) Landslide susceptibility zonation mapping and its validation in part of Garhwal Lesser Himalaya, India, using Binary Logistic Regression analysis and receiver operating characteristic curve method. Landslides 6: 17–26.CrossRefGoogle Scholar
  25. Naranjo JL, van Western CJ, Soeters R (1994) Evaluating the use of training areas in bivariate statistical landslide hazard analysis: a case study in Colombia. Journal of Institute for Aerospace Survey and Earth Science 3: 292–300.Google Scholar
  26. 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. Engineering Geology 97(3/4): 171–191.CrossRefGoogle Scholar
  27. Nefeslioglu HA, Sezer E, Gokceoglu C, Bozkir AS, Duman TY (2010) Assessment of landslide susceptibility by decision trees in the metropolitan area of Istanbul, Turkey. Mathematical Problems in Engineering, doi: 10.1155/2010/901095Google Scholar
  28. Olchmaher CG, Davis CJ (2003) Using multiple regression and GIS technology to predict landslide hazard in northeast Kansas, USA. Engineering Geology 69: 331–343.CrossRefGoogle Scholar
  29. Pachauri AK and Pant M. (1992) Landslide hazard mapping based on geological attributes. Engineering Geology 32: 81–100.CrossRefGoogle Scholar
  30. Pradhan B (2010) Manifestation of an advanced fuzzy logic model coupled with Geo-information techniques to landslide susceptibility mapping and their comparison with logistic regression modeling. Environmental Ecological Statistics, doi: 10.1007/s10651-010-0147-7.Google Scholar
  31. Pradhan B, Lee S (2009a) Delineation of landslide hazard areas using frequency ratio, logistic regression and artificial neural network model at Penang Island, Malaysia. Environmental Earth Science, doi:10.1007/s12665-009-0245-8.Google Scholar
  32. Pradhan B, Lee S (2009b) Landslide risk analysis using artificial neural network model focusing on different training sites. International Journal Physical Science 3(11): 1–15.Google Scholar
  33. Pradhan B, Lee S, Buchroithner MF (2010a) A GIS-based backpropagation neural network model and its cross application and validation for landslide susceptibility analyses. Computer Environment and Urban systems 34: 216–235.CrossRefGoogle Scholar
  34. Pradhan B, Sezer, EA, Gokceoglu, C, Buchroithner, MF. (2010b) Landslide susceptibility mapping by neuro-fuzzy approach in a landslide prone area (Cameron Highland, Malaysia). IEEE Transactions on Geoscience and Remote Sensing 48(12):4164–4177.CrossRefGoogle Scholar
  35. Sezer, E.A., Pradhan, B., Gokceoglu, C. 2011. Manifestation of an adaptive neuro-fuzzy model on landslide susceptibility mapping: Klang valley, Malaysia. Expert Systems with Applications 38(7): 8208–8219.CrossRefGoogle Scholar
  36. Süzen ML, Doyuran V (2004) A comparison of the GIS based landslide susceptibility assessment methods: multivariate versus bivariate. Environmental Geology 45:665–679CrossRefGoogle Scholar
  37. Yesilnacar E, Topal T (2005) Landslide susceptibility mapping: A comparison of logistic regression and neural networks in a medium scale study, Hendek region (Turkey). Engineering Geology 79(3–4): 251–266.CrossRefGoogle Scholar
  38. Yilmaz I (2010) Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks and support vector machine. Environmental Earth Science 61(4): 821–836.CrossRefGoogle Scholar
  39. Zhu Lei, Huang Jing-Feng (2006) GIS-based logistic regression method for landslide susceptibility mapping on regional scale. Journal of Zhejiang University Science A 7(12): 2001–2017.CrossRefGoogle Scholar

Copyright information

© Science Press, Institute of Mountain Hazards and Environment, CAS and Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Sujatha E. Ramani
    • 1
    Email author
  • Kumarvel Pitchaimani
    • 2
  • Victor Rajamanickam Gnanamanickam
    • 3
  1. 1.School of Civil EngineeringSASTRA UniversityThanjavurIndia
  2. 2.Indian Institute of AstrophysicsKodaikkanalIndia
  3. 3.Sairam Group of InstitutionsWest Tambaram, ChennaiIndia

Personalised recommendations