Statistical Approaches for Landslide Susceptibility Assessment and Prediction by Sujit Mandal & Subrata Mondal

Author:Sujit Mandal & Subrata Mondal
Language: eng
Format: epub
ISBN: 9783319938974
Publisher: Springer International Publishing

(Eq. 3.2)

where LSI= Landslide susceptibility index; Fr= Frequency ratio/rating to each class/range of landslide triggering factor.

3.2.2 Information Value Model (IVM)

According to Pereira et al. (2012), information value model (IVM) to evaluate the role of different combinations of landslide predisposing factors in the occurrence of shallow landslides in different parts of Northern Portugal. Kanungo et al. (2009), Champatiray (2000), Champatiray et al. (2007), and Arora et al. (2004) have opined that information value model (IVM) has proved useful method in determining the degree of influence of individual causative factor responsible for landslide occurrence. A modified form of pixel based information value model was also applied to assess landslide susceptibility mapping by Balsubramani and Kumaraswamy (2013). Some problems were ascertained in IV model, i.e. when no landslide exists in certain subclass, there is not any significance of that particular class. Researchers usually assigned 0 or “no pixel data” to those pixels (class), which would make the results much more exaggerated if a large number of those pixels existed, and since “0” value in the model means that the ratio of landslide pixels in subclass I is equal to the average ratio of the study area, and if no landslides existed, the results should approach infinitesimal. To overcome this problem, was not calculated and was qualitatively determined as the lower information value considering the data set of predisposing variables, which could avoid the problem of high exaggeration, while the results could not exactly show the information value of this area by Oliveira et al. (2015). It showed that MIV model can tackle the problem of “no pixel data in subclass” well, generate the true information value, and show real running trend, which performs well in showing the relationship between predisposing factors and landslide occurrence and can be used for preliminary landslide susceptibility assessment in the study area. In the present study, modified information value model was applied to assess slope instability of Darjeeling Himalaya.

Spatial distribution of landslide susceptibility was analysed with reasonable accuracy using information value model on GIS platform by Vijith et al. (2009), Sarkar et al. (2006), Zezere (2002), Wang and Sassa (2005), Sharma et al. (2009), Akbar and Ha (2011), and Pereira et al. (2012). The information value (IV) for each subclass of the factors was calculated with the help of following equation (eq. 3.3).

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