Determining the Suitability of Two Different Statistical Techniques in Shallow Landslide (Debris Flow) Initiation Susceptibility Assessment in the Western Ghats

  • M. V. Ninu Krishnan State Emergency Operation Centre (SEOC), Kerala State Disaster Management Authority, Institute of Land and Disaster Management, Kerala, India
  • P. Pratheesh Centre for Geoinformation Science and Technology, University of Kerala, Kariavattom, Kerala, India
  • P. G. Rejith Department of Environment and Climate Change, Government of Kerala, Kerala, India
  • H. Vijith Department of Applied Geology, School of Engineering and Science, Curtin University of Technology, Sarawak, Malaysia
Keywords: Western Ghats, shallow landslide, information value, multiple logistic regression, susceptibility assessment

Abstract

In the present study, the Information Value (InfoVal) and the Multiple Logistic Regression (MLR) methods based on bivariate and multivariate statistical analysis have been applied for shallow landslide initiation susceptibility assessment in a selected subwatershed in the Western Ghats, Kerala, India, to determine the suitability of geographical information systems (GIS) assisted statistical landslide susceptibility assessment methods in the data constrained regions. The different landslide conditioning terrain variables considered in the analysis are geomorphology, land use/land cover, soil thickness, slope, aspect, relative relief, plan curvature, profile curvature, drainage density, the distance from drainages, lineament density and distance from lineaments. Landslide Susceptibility Index (LSI) maps were produced by integrating the weighted themes and divided into five landslide susceptibility zones (LSZ) by correlating the LSI with general terrain conditions. The predictive performances of the models were evaluated through success and prediction rate curves. The area under success rate curves (AUC) for InfoVal and MLR generated susceptibility maps shows 84.11% and 68.65%, respectively. The prediction rate curves show good to moderate correlation between the distribution of the validation group of landslides and LSZ maps with AUC values of 0.648 and 0.826 respectively for MLR and InfoVal produced LSZ maps. Considering the best fit and suitability of the models in the study area by quantitative prediction accuracy, LSZ map produced by the InfoVal technique shows higher accuracy, i.e. 82.60%, than the MLR model and is more realistic while compared in the field and is considered as the best suited model for the assessment of landslide susceptibility in areas similar to the study area. The LSZ map produced for the area can be utilised for regional planning and assessment process, by incorporating the generalised rainfall conditions in the area.

DOI: http://dx.doi.org/10.5755/j01.erem.70.4.8510

Author Biographies

M. V. Ninu Krishnan, State Emergency Operation Centre (SEOC), Kerala State Disaster Management Authority, Institute of Land and Disaster Management, Kerala, India
Research fellow at State Emergency Operation Centre (SEOC), Kerala State Disaster Management Authority, Institute of Land and Disaster Management, Kerala, India
P. Pratheesh, Centre for Geoinformation Science and Technology, University of Kerala, Kariavattom, Kerala, India
Project scientist at Centre for Geoinformation Science and Technology, University of Kerala, Kariavattom, Kerala, India
P. G. Rejith, Department of Environment and Climate Change, Government of Kerala, Kerala, India
Environmental officer at Department of Environment and Climate Change, Government of Kerala, Kerala, India
H. Vijith, Department of Applied Geology, School of Engineering and Science, Curtin University of Technology, Sarawak, Malaysia
Senior postdoc fellow at Department of Applied Geology, School of Engineering and Science, Curtin University of Technology, Sarawak, Malaysia
Published
2015-01-29
Section
Articles