Application of Analytical Hierarchy Process (AHP) and Frequency Ratio (FR) Model in Assessing Landslide Susceptibility and Risk - Semi-quantitative Approaches for Landslide Assessment and Prediction - page 189

Keywords Landslide susceptibility Landslide risk Analytical hierarchy process

(AHP) Frequency ratio (FR) RS and GIS Accuracy assessment

7.1 Introduction

The identi cation of the causative factors is the basis of many methods of landslide

susceptibility assessment. In most of the cases, the landslide is the critical mech-
anism of erosional processes and in such condition, landslide is inevitable and
necessary part of the natural landscape process system. Although the occurrences of
landslide hazards and its impact on human society cannot be prevented fully by
analyzing the slope stability condition, but the better understanding of geo-technical
attributes of the soil can contribute to greater knowledge and understanding about
the spatial distribution of slope instability which are very much essential for land
use planning. Landslides are the results of two interacting sets of forces; the pre-
condition factors, naturally induced which govern the stability conditions of slopes,
and the preparatory and triggering factors, induced either by natural factors or by
human intervention. Landslide analysis is mainly done by assessing Susceptibility,
Hazard and Risk (Einstein 1988 ). RS and GIS based landslide hazard zonation
approach had been studied by Anabalangan ( 1992 ), Muthu and Petrou ( 2007 ) and
Caiyan and Jianping ( 2009 ). Rowbothan and Dudycha ( 1998 ), Donati and Turrini
( 2002 ), Lee and Choi ( 2003 ), Lee et al. ( 2004a , b ), Lee and Pradhan ( 2006 , 2007 ),
Pradhan and Lee ( 2010a , b , c ), Sarkar and Kanungo ( 2004 ), Shari kia ( 2007 ),

Pande et al. ( 2008 ) and Nithya and Prasanna ( 2010 ) studied and applied the
probabilistic model for landslide susceptibility and risk evaluation. Guzzetti et al.
( 1999a ) summarized many landslide hazard evaluation studies. Jibson et al. ( 2000 )
and Zhou et al. ( 2002 ) applied the probabilistic models for landslide risk and hazard
analysis. Atkinson and Massari ( 1998 ) and Vijith and Madhu ( 2008 ) introduced the
logistic regression model for landslide hazard mapping. Landslide hazards were
evaluated by using fuzzy logic, and arti

cial neural network models were used in
the works of Gokceoglu et al. ( 2000 ) and Pistocchi et al. ( 2002 ). Landslide
Susceptibility mapping using either multivariate or bivariate statistical approach
considered the historical link between landslide controlling factors and the distri-
bution of landslides (Guzzetti et al. 1999b , c ).
The models in connection to the slope stability, shallow and deep seated land-
slides were introduced and veri ed by Varnes ( 1958 ), Young ( 1963 ), Vanmarcke

( 1977 ), Burton and Bathrust ( 1998 ), Bradinoni and Church ( 2004 ). The geotectonic
factors of slope instability were studied in details by Brudsen ( 1979 ), Windisch
( 1991 ), Carson ( 1975 , 1977 ) and Borga et al. ( 1998 ). Comprehensive list of sta-
bility factors commonly employed in the factors mapping approach was prepared
by Crozier ( 1986 ) and Tiwari and Marui ( 2001 , 2002 , 2003 , 2004 ). Analytical
Hierarchy Process (AHP), a semi-quantitative method based on decomposition,
comparative judgement, and synthesis of priorities are often very much useful for