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

M V Ninu Krishnan,P G Rejith,H Vijith,P Pratheesh

doi:10.5755/j01.erem.70.4.8510

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

Highlights

Landslides are considered as the most disastrous hydro-geological phenomena which frequently occur in association with extreme climatic and geologic events
The present study aims to assess the usability of geographical information systems (GIS) assisted statistical landslide susceptibility models in a data deficient hilly terrain in the Western Ghats of Kerala, India, which witnesses severe land degradation due to landslides during the monsoon periods
The number of landslide pixels falling in each class of the thematic data layers has been recorded and weights have been calculated on the basis of Information Value (InfoVal) and multiple logistic regression (MLR) techniques

Summary

Introduction

Landslides are considered as the most disastrous hydro-geological phenomena which frequently occur in association with extreme climatic and geologic events. The rapid development of spatial information technologies, especially the availability of high resolution remote sensing images, digital elevation models and powerful application of geographical information systems (GIS) together made great advancement in mapping and assessment of landslide susceptibility of an area using different methods (Carrara et al, 1991; Brenning, 2005; van Westen et al, 2006; Akbar and Ha, 2011; Akgun et al, 2012; Althuwaynee et al, 2010; Pourghasemi et al, 2013; Ozdemir and Altural, 2013). To remove subjectivity in qualitative analysis, various statistical methods have been used in LSZ studies The aim of these methods is to identify areas that are susceptible to future landsliding, based on the knowledge of past landslide events, geological attributes, terrain parameters and other environmental conditions that are associated with the presence or absence of such phenomena. Comparison of various methods used for assessing landslide susceptibility of a terrain can be found at Aleotti and Chowdhury (1999), Guzzetti et al (1999), van Westen (2000) and Huabin et al (2005)

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Results

Conclusion