Comparative analysis in GIS-based landslide hazard zonation—a case study in Bodi-Bodimettu Ghat section, Theni District, Tamil Nadu, India

The aim of this research article is to assess and compare the reliability of spatial-based different landslide hazard zonation mapping methods in the Bodi-Bodimettu Ghat section, Theni District as a case study. In favor of this intention, the three methods like the Bureau of Indian Standard (BIS), multi-criteria analysis (MCA), and frequency ratio (FR) model have been applied to find out three different landslide hazard zonation maps. The results of the three methods were compared using parameters such as landslide density, success rate curve, and spatially agreed area. Approximately the same variation is seen in the final results of BIS in comparison to the other methods, namely MCA and FR. But if we go actually into the detail, low hazard and moderate hazard seem to merge to indicate one single zone made as moderate hazard. Moreover, the nature of weightages used in different techniques may have the same influence on the outcome of the result.

[1]  Manoj K. Arora,et al.  Approaches for comparative evaluation of raster GIS-based landslide susceptibility zonation maps , 2008, Int. J. Appl. Earth Obs. Geoinformation.

[2]  Manoj K. Arora,et al.  A comparative study of conventional, ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in Darjeeling Himalayas , 2006 .

[3]  Florimond De Smedt,et al.  Evaluation and comparison of GIS based landslide susceptibility mapping procedures in Kulekhani watershed, Nepal , 2013, Journal of the Geological Society of India.

[4]  D. P. Kanungo,et al.  An Integrated Approach for Landslide Susceptibility Mapping Using Remote Sensing and GIS , 2004 .

[5]  D. Varnes SLOPE MOVEMENT TYPES AND PROCESSES , 1978 .

[6]  E. Yesilnacar,et al.  Landslide susceptibility mapping : A comparison of logistic regression and neural networks methods in a medium scale study, Hendek Region (Turkey) , 2005 .

[7]  Saro Lee Application of Likelihood Ratio and Logistic Regression Models to Landslide Susceptibility Mapping Using GIS , 2004, Environmental management.

[8]  H. A. Nefeslioglu,et al.  An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps , 2008 .

[9]  B. Pradhan,et al.  Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models , 2007 .

[10]  C. Chung,et al.  Probabilistic prediction models for landslide hazard mapping , 1999 .

[11]  D. Varnes,et al.  Landslide types and processes , 2004 .

[12]  Manfred F. Buchroithner,et al.  Landslide Susceptibility Mapping by Neuro-Fuzzy Approach in a Landslide-Prone Area (Cameron Highlands, Malaysia) , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[13]  Irasema Alcántara-Ayala,et al.  Modelling mass failure in a Mediterranean mountain environment: climatic, geological, topographical and erosional controls , 1998 .

[14]  Saro Lee,et al.  Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models , 2006 .

[16]  Biswajeet Pradhan,et al.  Comparison between prediction capabilities of neural network and fuzzy logic techniques for L and slide susceptibility mapping. , 2010 .

[17]  Manoj K. Arora,et al.  GIS based Landslide Hazard Zonation using Neuro-Fuzzy Weighting , 2005, IICAI.

[18]  R. Anbalagan,et al.  Landslide hazard evaluation and zonation mapping in mountainous terrain , 1992 .

[19]  E. Saranathan,et al.  Macro Landslide Hazard Zonation Mapping - Case Study from Bodi – Bodimettu Ghats Section, Theni District, Tamil Nadu - India , 2011 .

[20]  Saro Lee,et al.  Landslide susceptibility analysis and its verification using likelihood ratio, logistic regression, and artificial neural network models: case study of Youngin, Korea , 2007 .

[21]  V. Doyuran,et al.  A comparison of the GIS based landslide susceptibility assessment methods: multivariate versus bivariate , 2004 .

[22]  R. Soeters,et al.  Use of Geomorphological Information in Indirect Landslide Susceptibility Assessment , 2003 .

[23]  J. Corominas,et al.  Assessment of shallow landslide susceptibility by means of multivariate statistical techniques , 2001 .

[24]  E. Saranathan,et al.  Landslide vulnerability mapping using frequency ratio model: a geospatial approach in Bodi-Bodimettu Ghat section, Theni district, Tamil Nadu, India , 2013, Arabian Journal of Geosciences.

[25]  E. Sujatha,et al.  Landslide susceptibility analysis using probabilistic likelihood ratio model—a geospatial-based study , 2013, Arabian Journal of Geosciences.

[26]  B. Pradhan,et al.  Probabilistic landslide hazards and risk mapping on Penang Island, Malaysia , 2006 .

[27]  Lee H. MacDonald,et al.  Runoff and road erosion at the plot and road segment scales, St John, US Virgin Islands , 2001 .