Landslide susceptibility analysis using Probabilistic Certainty Factor Approach: A case study on Tevankarai stream watershed, India

Abstract

This paper reports the use of a GIS based Probabilistic Certainty Factor method to assess the geo-environmental factors that contribute to landslide susceptibility in Tevankarai Ar sub-watershed, Kodaikkanal. Landslide occurrences are a common phenomenon in the Tevankarai Ar sub-watershed, Kodaikkanal owing to rugged terrain at high altitude, high frequency of intense rainfall and rapidly expanding urban growth. The spatial database of the factors influencing landslides are compiled primarily from topographical maps, aerial photographs and satellite images. They are relief, slope, aspect, curvature, weathering, soil, land use, proximity to road and proximity to drainage. Certainty Factor Approach is used to study the interaction between the factors and the landslide, highlighting the importance of each factor in causing landslide. The results show that slope, aspect, soil and proximity to roads play important role in landslide susceptibility. The landslide susceptibility map is classified into five susceptible classes-low, very low, uncertain, high and very high − 93.32% of the study area falls under the stable category and 6.34% falls under the highly and very highly unstable category. The relative landslide density index (R index) is used to validate the landslide susceptibility map. R index increases with the increase in the susceptibility class. This shows that the factors selected for the study and susceptibility mapping using certainty factor are appropriate for the study area. Highly unstable zones show intense anthropogenic activities like high density settlement areas, and busy roads connecting the hill town and the plains.

Predictive modeling of landslide hazards in Wen County, northwestern China based on information value, weights-of-evidence, and certainty factor

“…Over the years, both types of methods have been used in conjunction with geographic information systems (GIS) to assess landslide susceptibility (Dahal et al 2008;Regmi et al 2010;Mohammady et al 2012;Kayastha et al 2013;Bourenane et al 2015;Sujatha 2017;Sujatha and Sridhar 2017). There are many statistical models used in landslide hazard analysis, including the frequency ratio method (Mohammady et al 2012;Kayastha 2015;Bourenane et al 2016;Chen et al 2017a), statistical index method (Bui et al 2011;Pourghasemi et al 2013a;Razavizadeh et al 2017), weights of evidence method (Dahal et al 2008;Razavizadeh et al 2017), certainty factor method (Kanungo et al 2011;Sujatha et al 2012), and logistic regression method (Ayalew and Yamagishi 2005;Lee and Pradhan 2007;Yilmaz 2009;Chen et al 2017c). Other varieties of classification techniques, such as fuzzy systems (Oh and Pradhan 2011;Sezer et al 2011), decision-trees (Saito et al 2009;Pradhan 2013), neural networks (Yesilnacar and Topal 2005;Ermini et al 2005;Pham et al 2017), and support vector machines (Yao et al 2008;Pradhan 2013), have been used to assess landslide risk.…”

Investigation of seepage effect on river dike’s stability under steady state and transient conditions

“…probability exceedance). So having sufficient information on earth structures and specifically on dike performance is very important in order to have clear picture about dike stability against different factor that cause failure of structure itself, [9], [10]. In this paper, seepage effects on dike structures stability during steady state and transient conditions are discussed.…”

Validation of Spatial Prediction Models for Landslide Susceptibility Mapping by Considering Structural Similarity

“…The CF for each pixel is defined as the change in certainty that a proposition is true from without the evidence (prior probability of having landslide in the study area) to be given the evidence (conditional probability of having a landslide given a certain class of a thematic layer) for each data layer [49]. The CF, as a function of probability, originally proposed by Shortliffe and Buchanan, [53] ) where CF is the certainty factor, ppa is the conditional probability of having a number of landslides in a class (e.g., south-facing slope in the aspect layer, Quaternary deposits in the lithology layer) and pps is the prior probability of having the total number of landslides in the study area.…”

“…In the frequency ratio model, input processes, calculations, and output processes are very simple and can be readily understood. In addition, among the commonly used GIS analysis models for landslide susceptibility, the certainty factor has also been widely considered and experimentally investigated in the literature [48,49]. Therefore, in this study, the landslide susceptibility analyses were implemented using these two methods.…”