Combining spatial data in landslide reactivation susceptibility mapping: A likelihood ratio-based approach in W Belgium

Dewitte, Olivier; Chung, Chae-Heon; Cornet, Yves; Daoudi, Mohamed; Demoulin, Alain

doi:10.1016/j.geomorph.2010.06.010

Cited by 33 publications

(25 citation statements)

References 54 publications

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“…Budimir et al (2015) mentions that in a total of 37 parameters usually used slope, aspect, and lithology, are significantly used particularly on studies related to rainfall-induced landslides. In fact, the relevance of the spatial data combination used in the prediction became an important issue in landslide susceptibility mapping (Dewitte et al 2010).…”

Section: Landslide Causal Factorsmentioning

confidence: 99%

Performance of frequency ratio and logistic regression model in creating GIS based landslides susceptibility map at Lompobattang Mountain, Indonesia

Rasyid

Bhandary

Yatabe

2016

Geoenviron Disasters

175

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The purposes of this study is to create a landslide susceptibility map (LSM) for Lompobattang Mountain area in Indonesia. The foot of the Lompobattang Mountain area suffered flash flood and landslides in 2006, which led to significant adverse impact on the nearby settlements. There were 158 identified landslides covering a total area of 3.44 km 2 . Landslide inventory data were collected using google earth image interpretations. The landslide inventories were prepared out of the past landslide events, and future landslide occurrence was predicted by correlating landslide causal factors. In this study landslide inventories were divided into landslide data for training and landslide data for validation. The LSM was prepared by Frequency Ratio (FR) and Logistic Regression (LR) statistical methods. Lithology, distance from the road, distance from the river, distance from the fault, land use, curvature, aspect, and slope degree were used as conditioning parameters. Area under the curve (AUC) of the Receiver Operating Characteristic (ROC) was used to check the performance of the models. In the analysis, the FR model results in 85.8 % accuracy in the AUC success rate while the LR model was found to have 86.9 % accuracy. However, the accuracy of both these models in AUC predictive rate is the same at around 85.1 %. The LR model is 6.34 % higher than the FR model in comparison to its accuracy for ratio of landslide validation. The landslide susceptibility map consist of the predicted landslide area, hence it can be used to reduce the potential hazard associated with the landslides in this study area.

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Section: Landslide Causal Factorsmentioning

confidence: 99%

Performance of frequency ratio and logistic regression model in creating GIS based landslides susceptibility map at Lompobattang Mountain, Indonesia

Rasyid

Bhandary

Yatabe

2016

Geoenviron Disasters

175

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“…Since fuzzy set theory does not provide the functions for generating fuzzy membership values of landslide conditioning factors and theirs classes, therefore various methods have been proposed to address this issue. Champati ray et al (2007) and Blais-Stevens et al (2012) used expert knowledge; Lee (2007), Pradhan (2010Pradhan ( , 2011aPradhan ( , 2011b, and Pourghasemi et al (2012a) used the frequency ratio; Ercanoglu and Gokceoglu (2004) and Kanungo et al (2008Kanungo et al ( , 2009) used the Cosine amplitude; Chung and Fabbri (2008) and Dewitte et al (2010) used the likelihood ratio function. Similarly, Pradhan et al (2010b), Sezer et al (2011), and Tien Bui et al (2012d) used the neural network method.…”

Section: Introductionmentioning

confidence: 99%

A novel hybrid evidential belief function-based fuzzy logic model in spatial prediction of rainfall-induced shallow landslides in the Lang Son city area (Vietnam)

Bui

Pradhan

Revhaug

et al. 2013

Geomatics, Natural Hazards and Risk

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(2015) A novel hybrid evidential belief function-based fuzzy logic model in spatial prediction of rainfall-induced shallow landslides in the Lang Son city area (Vietnam), Geomatics, Natural Hazards and Risk, 6:3, 243-271, DOI: 10.1080/19475705.2013 The main objective of this study is to investigate potential application of an integrated evidential belief function (EBF)-based fuzzy logic model for spatial prediction of rainfall-induced shallow landslides in the Lang Son city area (Vietnam). First, a landslide inventory map was constructed from various sources. Then the landslide inventory map was randomly partitioned as a ratio of 70/30 for training and validation of the models, respectively. Second, six landslide conditioning factors (slope angle, slope aspect, lithology, distance to faults, soil type, land use) were prepared and fuzzy membership values for these factors classes were estimated using the EBF. Subsequently, fuzzy operators were used to generate landslide susceptibility maps. Finally, the susceptibility maps were validated and compared using the validation dataset. The results show that the lowest prediction capability is the fuzzy SUM (76.6%). The prediction capability is almost the same for the fuzzy PRODUCT and fuzzy GAMMA models (79.6%). Compared to the frequency-ratio based fuzzy logic models, the EBF-based fuzzy logic models showed better result in both the success rate and prediction rate. The results from this study may be useful for local planner in areas prone to landslides. The modelling approach can be applied for other areas.

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“…In the analysis, scarp areas of landslides were taken in consideration (Dewitte et al 2010;Gorsevski and Jankowski 2010). In this study, landslide scarp areas were first identified as polygons.…”

Section: Application Of Weights Of Evidence Methodsmentioning

confidence: 99%

“…Conditions that cause landslides (instability factors), or are directly or indirectly linked to slope failures, can be collected and used to build the predictive models of landslide occurrence (Dietrich et al 1995). Landslide susceptibility is the likelihood of a landslide occurring (Dewitte et al 2010) in an area on the basis of the local terrain conditions. It is the degree to which a terrain can be affected by slope movements, i.e.…”

Section: Introductionmentioning

confidence: 99%

“…The landslide susceptibility map is based on the distribution of past landslides, slope steepness, type of soil/bedrock, structure, hydrology and other pertinent data (Nandi and Shakoor 2009). Many different methods and techniques for evaluating landslide susceptibility have been proposed and tested (Akgun et al 2008;Atkinson and Massari 2011;Brenning 2005;Clerici et al 2006;Dahal et al 2008;Dewitte et al 2010;Ercanoglu et al 2008;Ghosh and Carranza 2010;Gorsevski and Jankowski 2010;Jaiswal et al 2010;Lee 2005;Lee and Dan 2005;Pradhan 2006, 2007;Nefeslioglu et al 2008;Oh et al 2009;Oh and Pradhan 2011;Ozdemir 2009;Pirasteh et al 2009;Pistocchi et al 2002;Pradhan et al 2006Pradhan et al , 2010aPradhan and Youssef 2010;Rossi et al 2010;Saha et al 2005a, b;Sezer et al 2011;Sterlacchini et al 2011;van Den Eeckhaut et al 2009van Westen 2004;Yeon et al 2010;Yilmaz 2009Yilmaz , 2010Youssef et al 2009;Zhou et al 2002). General overviews of research into landslide susceptibility can be found in the work of Leroi (1996), Aleotti and Chowdhury (1999), Guzzetti et al (1999), Dai et al (2001), …”

Section: Introductionmentioning

confidence: 99%

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Landslide susceptibility mapping using Bayesian approach in the Sultan Mountains (Akşehir, Turkey)

Özdemir

2011

Nat Hazards

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Landslides cause heavy damage to property and infrastructure, in addition to being responsible for the loss of human lives in many parts of the Turkey. The paper presents GIS-based spatial data analysis for landslide susceptibility mapping in the regions of the Sultan Mountains, West of Akşehir, and central part of Turkey. Landslides occur frequently in the area and seriously affect local living conditions. Therefore, spatial analysis of landslide susceptibility in the Sultan Mountains is important. The relationships between landslide distributions with the 19 landslide affecting parameters were analysed using a Bayesian model. In the study area, 90 landslides were observed. The landslides were randomly subdivided into 80 training landslides and 10 test landslides. A landslide susceptibility map was produced by using the training landslides. The test landslides were used in the accuracy control of the produced landslide susceptibility map. Approximately 9% of the study area was classified as high susceptibility zone. Medium, low and very low susceptibility zones covered 8, 23 and 60% of the study area, respectively. Most of the locations of the observed landslides actually fall into moderate (17.78%) and high (77.78. %) susceptibility zones of the produced landslide susceptibility map. This validates the applicability of proposed methods, approaches and the classification scheme. The high susceptibility zone is along both sides of the Akşehir Fault and at the north-eastern slope of the Sultan Mountains. It was determined that the surface area of the Harlak and Deresenek formations, which have attained lithological characteristics of clayey limestone with a broken and separated base, and where area landslides occur, possesses an elevation of 1,100-1,600 m, a slope gradient of 25°-35°and a slope aspect of 22.5°-157.5°facing slopes.

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Combining spatial data in landslide reactivation susceptibility mapping: A likelihood ratio-based approach in W Belgium

Cited by 33 publications

References 54 publications

Performance of frequency ratio and logistic regression model in creating GIS based landslides susceptibility map at Lompobattang Mountain, Indonesia

Performance of frequency ratio and logistic regression model in creating GIS based landslides susceptibility map at Lompobattang Mountain, Indonesia

A novel hybrid evidential belief function-based fuzzy logic model in spatial prediction of rainfall-induced shallow landslides in the Lang Son city area (Vietnam)

Landslide susceptibility mapping using Bayesian approach in the Sultan Mountains (Akşehir, Turkey)

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