A comparison of information value and logistic regression models in landslide susceptibility mapping by using GIS

Chen, Tao; Niu, Ruiqing; Jia, Xiuping

doi:10.1007/s12665-016-5317-y

Cited by 94 publications

(54 citation statements)

References 59 publications

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“…In this model, information values of predisposing factors were used to characterize the possibility of landslides occurrence. The information value I( , H) of each landslide predisposing factor ( = 1,2, … , ) can be expressed as follows [21,36,37]: …”

Section: Information Value Model (Ivm)mentioning

confidence: 99%

An Improved Information Value Model Based on Gray Clustering for Landslide Susceptibility Mapping

Chen

Deng³

et al. 2017

IJGI

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Landslides, as geological hazards, cause significant casualties and economic losses. Therefore, it is necessary to identify areas prone to landslides for prevention work. This paper proposes an improved information value model based on gray clustering (IVM-GC) for landslide susceptibility mapping. This method uses the information value derived from an information value model to achieve susceptibility classification and weight determination of landslide predisposing factors and, hence, obtain the landslide susceptibility of each study unit based on the clustering analysis. Using a landslide inventory of Chongqing, China, which contains 8435 landslides, three landslide susceptibility maps were generated based on the common information value model (IVM), an information value model improved by an analytic hierarchy process (IVM-AHP) and our new improved model. Approximately 70% (5905) of the inventory landslides were used to generate the susceptibility maps, while the remaining 30% (2530) were used to validate the results. The training accuracies of the IVM, IVM-AHP and IVM-GC were 81.8%, 78.7% and 85.2%, respectively, and the prediction accuracies were 82.0%, 78.7% and 85.4%, respectively. The results demonstrate that all three methods perform well in evaluating landslide susceptibility. Among them, IVM-GC has the best performance.

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Section: Information Value Model (Ivm)mentioning

confidence: 99%

An Improved Information Value Model Based on Gray Clustering for Landslide Susceptibility Mapping

Chen

Deng³

et al. 2017

IJGI

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“…The IV method was applied in several landslide susceptibility zonation studies, providing good results (e.g., Che et al, 2012;Chen et al, 2016;Conforti et al, 2012) at the regional scale. This method was also applied in several studies conducted in Portuguese territory with good performance in the 10 susceptibility assessment (e.g., Guillard and Zêzere, 2012;Oliveira et al, 2015b;Pereira et al, 2014;Zêzere et al, 2017).…”

Section: Methodsmentioning

confidence: 99%

Land use and land cover geoinformation properties and its influence on the landslide susceptibility zonation of road network

Meneses

Pereira

Reis

2017

Preprint

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“…Fell et al 2008 considered the landslide susceptibility for identification of landslide prone sites and their relation to the set of causal factors in that area. The landslide susceptibility mapping generally involves two methods (I) qualitative which is based on expert knowledge and the landslide inventory development (Saha et al, 2002) such as analytical hierarchy process (AHP) used by many researchers (Komac 2006;Kayastha et al 2012;Wu et al 2016;Kumar and Anabalgan 2016;Achour et al 2017) (II) quantitative methods including bivariate and multivariate modeling methods for statistical evaluation of landslides occurrences (Yin and Yan 1988;Kumar et al 1993;Anbalagan and Singh 1996;Dai and Lee, 2002;Saha et al 2005; Lee and Sambath 2006;Mathew et al 2007;Dahal et al 2008;Singh et al 2008;Pradhan and Lee 2010;Rozos et al 2011;Yalcin et al 2011;Kayastha et al 2013;Bijukchhen et al 2013;Anbalagan et al, 2015;Rawat et al 2015;Sharma and Mahajan 2018;Chen et al 2016). In India landslide susceptibility mapping for Gharwal and Kumaun region of Uttarakhand has been carried out by Pachauri and Pant 1992;Gupta et al 1999;Anabalgan et al, 2008;Anbalagan et al, 2015;Anabalgan, 2016 whereas, Sarkar andKanungo, 2004;Sarkar et al 2013; have mapped the landslides of Darjeeling Himalaya for statistical correlation with the causal factors.…”

Section: Introductionmentioning

confidence: 99%

Comparative evaluation of GIS-based landslide susceptibility mapping using statistical and heuristic approach for Dharamshala region of Kangra Valley, India

Sharma

Mahajan

2018

Geoenviron Disasters

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Background: The Dharamshala region of Kangra valley, India is one of the fastest developing Himalayan city which is prone to landslide events almost around the year. The development is going on a fast pace which calls for the need of landslide susceptibility zonation studies in order to generate maps that can be used by planners and engineers to implement the projects at safer locations. A landslide inventory was developed for Dharamshala with help of the field observations. Based on field investigations and satellite image studies eight casual factors viz. lithology, soil, slope, aspect, fault buffer, drainage buffer, road buffer and land cover were selected to represent the landslide problems of the study area. The research presents the comparative assessment of geographic information system based landslide susceptibility maps using analytical hierarchy process and frequency ratio method. The maps generated have been validated and evaluated for checking the consistency in spatial classification of susceptibility zones using prediction rate curve, landslide density and error matrix methods. Results: The results of analytical hierarchy process (AHP) shows that maximum factor weightage results from lithology and soil i.e. 0.35 and 0.25. The frequency ratios of the factor classes indicate a strong correlation of Dharamsala Group of rock (value is 1.28) with the landslides which also agrees with the results from the AHP method where in the same lithology has the maximum weightage i.e. 0.71. The landslide susceptibility zonation maps from the statistical frequency ratio and heuristic analytical hierarchy process method were classified in to five classes: very low susceptibility, low susceptibility, medium susceptibility, high susceptibility and very high susceptibility. The landslide density distribution in each susceptibility class shows agreement with the field conditions. The prediction rate curve was used for assessing the future landslide prediction efficiency of the susceptibility maps generated. The prediction curves resulted the area under curve values which are 76.77% for analytical hierarchy process and 73.38% for frequency ratio method. The final evaluation of the susceptibility maps was based on the error matrix approach to calculate the area distributed among the susceptibility zones of each map. This technique resulted in assessing the spatial differences and agreement between both the susceptibility maps. The evaluation results show 70% overall spatial similarity between the resultant landslide susceptibility maps.

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A comparison of information value and logistic regression models in landslide susceptibility mapping by using GIS

Cited by 94 publications

References 59 publications

An Improved Information Value Model Based on Gray Clustering for Landslide Susceptibility Mapping

An Improved Information Value Model Based on Gray Clustering for Landslide Susceptibility Mapping

Land use and land cover geoinformation properties and its influence on the landslide susceptibility zonation of road network

Comparative evaluation of GIS-based landslide susceptibility mapping using statistical and heuristic approach for Dharamshala region of Kangra Valley, India

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