Terrigenous Mass Movements by Biswajeet Pradhan & Manfred Buchroithner

Author:Biswajeet Pradhan & Manfred Buchroithner
Language: eng
Format: epub
Publisher: Springer Berlin Heidelberg, Berlin, Heidelberg

Keywords

LandslideSusceptibilityEnsembleGISKorea

7.1 Introduction

Landslides are a major hazard, often causing property damage and economic losses and creating high maintenance costs. Landslides are triggered by different factors, either natural or related to human activities. Among natural factors, rainfall is certainly one of the most frequent causes of shallow landslide occurrence of the flow type in granular soil. Thus, it is necessary to assess landslide susceptibility to support forecasting of the phenomena. Landslide susceptibility is defined as areas likely to have slope failures in the future. It is estimated by correlating some of the principal factors that have contributed to past landslides (Brabb 1984; Guzzetti et al. 2005). In mathematical form, it is defined by correlating landslide density with different combinations of the factors (Clerici et al. 2002; Guzzetti et al. 2005).

Many methods have been proposed to assess landslide susceptibility, with increasing use of geographic information systems (GIS) using different models. These examples, many of these studies have applied probabilistic models such as frequency ratio, weight of evidence, etc. (Audisio et al. 2009; Dahal et al. 2008; Lee and Min 2001; Lee and Pradhan 2006; Lee et al. 2004a; Mousavi et al. 2009; Oh et al. 2009; Ozdemir 2009; Pirasteh et al. 2009; Regmi et al. 2010; Vahidnia et al. 2009; Yalcin 2008; Yilmaz 2009b, c, 2010). One of the statistical models available, the logistic regression model, has also been applied to landslide susceptibility mapping (Akgun and Bulut 2009; Bai et al. 2010, 2011; Chauhan et al. 2010; Das et al. 2010; Dominguez-Cuesta et al. 2010; Dong et al. 2009; Falaschi et al. 2009; Lee 2005, 2007a; Legorreta Paulin and Bursik 2009; Mathew et al. 2009; Nandi and Shakoor 2010; Oh and Lee 2010; Pradhan and Lee 2010a, b). More sophisticated assessments involved fuzzy logic, artificial neural network, AHP, Dempster-Shapfer theory of evidence, Monte Carlo methods also have been applied (Akgun and Türk 2010; Chen et al. 2009a, b; He and Fu 2009; Kanungo et al. 2008, 2009; Kawabata and Bandibas 2009; Lee 2007b; Lee and Evangelista 2006; Lee et al. 2006, 2004b; Liu et al. 2009; Melchiorre et al. 2008; Miles and Keefer 2009; Muthu et al. 2008; Park 2011; Poudyal et al. 2010; Pradhan and Lee 2010c, 2009, 2007; Prabu and Ramakrishnan 2009; Pradhan et al. 2010; Shafri et al. 2010; Tangestani 2009; Wang et al. 2009; Yilmaz 2009a). For the same study area, the frequency ratio, weight of evidence, logistic regression and, artificial neural network models were applied and compared (Lee et al. 2011).

Any attempt to quantitatively evaluate landslide susceptibility needs validation (Guzzetti et al. 2006). Validation uses the same geographic data, with independent landslides not used to construct the model. Most previous research presents an individual susceptibility model, discusses factors, and validates the susceptibility maps as an assessment of the model’s performance. Although various models and their application to landslide susceptibility have been published, the ensemble study involving combination of susceptibility maps from frequency ratio, weight of evidence, logistic regression and artificial neural network models has not been used for landslide susceptibility mapping purposes previously to improve prediction accuracy.

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