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

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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 %.

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Reference

  • Aleotti P, Chowdhury R (1999) Landslide hazard assessment: summary, review and new perspectives. Bulletin of Engineering Geology and Environment 58(1): 21–44.

    Article  Google Scholar 

  • Anbalagan R (1992) Landslide hazard evaluation and zonation mapping in mountainous terrain. Engineering Geology 32:269–277.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Chung CF, Fabbri AG (1999) Probabilistic prediction models for landslide hazard mapping. Photogrammetric Engineering and Remote Sensing 65: 1389–1399.

    Google Scholar 

  • Dai FC, Lee CF(2002) Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong Geomorphology 42: 213–28.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Ermini L, Catani F, Casagli N (2005) Artificial Neural Networks applied to landslide susceptibility assessment. Geomorphology 66: 327–343.

    Article  Google Scholar 

  • 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.

  • 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.

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Gomez H, Kavzoglu T (2005) Assessment of shallow landslide susceptibility using artificial neural network in Jabonosa River basin, Venezuela. Engineering Geology 78: 11–27.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Lee S (2007a) Application and verification of fuzzy algebraic operators to landslide susceptibility mapping. Environmental Geology 52: 615–623.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Lee S, Evangelista DG (2006) Earthquake induced landslide susceptibility mapping using an artificial neural network. Natural Hazards and Earth System Sciences 6: 687–695.

    Article  Google Scholar 

  • Lee S, Pradhan B (2007) Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides 4: 33–41.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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: 10.1155/2010/901095

  • Olchmaher CG, Davis CJ (2003) Using multiple regression and GIS technology to predict landslide hazard in northeast Kansas, USA. Engineering Geology 69: 331–343.

    Article  Google Scholar 

  • Pachauri AK and Pant M. (1992) Landslide hazard mapping based on geological attributes. Engineering Geology 32: 81–100.

    Article  Google Scholar 

  • 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.

  • 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.

  • 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 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Süzen ML, Doyuran V (2004) A comparison of the GIS based landslide susceptibility assessment methods: multivariate versus bivariate. Environmental Geology 45:665–679

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Civil Engineering, SASTRA University, Thanjavur, Tamilnadu, India

    Sujatha E. Ramani

  2. Indian Institute of Astrophysics, Kodaikkanal, Tamilnadu, India

    Kumarvel Pitchaimani

  3. Sairam Group of Institutions, West Tambaram, Chennai, Tamilnadu, India

    Victor Rajamanickam Gnanamanickam

Authors
  1. Sujatha E. Ramani
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  2. Kumarvel Pitchaimani
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  3. Victor Rajamanickam Gnanamanickam
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Correspondence to Sujatha E. Ramani.

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Ramani, S.E., Pitchaimani, K. & Gnanamanickam, V.R. GIS based landslide susceptibility mapping of Tevankarai Ar sub-watershed, Kodaikkanal, India using binary logistic regression analysis. J. Mt. Sci. 8, 505–517 (2011). https://doi.org/10.1007/s11629-011-2157-9

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  • DOI: https://doi.org/10.1007/s11629-011-2157-9

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