Weights of Evidence Method for Landslide Susceptibility Mapping in Takengon, Central Aceh, Indonesia

Pamela,; Sadisun, Imam A.; Arifianti, Yukni

doi:10.1088/1755-1315/118/1/012037

Cited by 24 publications

(18 citation statements)

References 5 publications

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“…The second method used to model landslide susceptibility is the bivariate WOE method. It is a log-linear version of general Bayesian theory (Bonham-Carter et al 1989;Bonham-Carter 1994), and it is frequently used to determine the significance of the impact of instability factors on the occurrence of mass movements (including Van Westen 1993; Lee et al 2002;Van Westen et al 2003;Klingseisen and Leopold 2006;Blahut 2009;Pamela et al 2018;Liu and Duan 2018). This model is based on the following formulas:…”

Section: Landslide Susceptibility Analysismentioning

confidence: 99%

Landslide susceptibility mapping of Gdynia using geographic information system-based statistical models

Małka

2021

Nat Hazards

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This work aims to prepare a reliable landslide susceptibility model and to analyse the factors contributing to landslides in a dynamic environment by considering the city of Gdynia, Poland as a case study. Geological, geomorphological, hydrological, hydrogeological, and anthropogenic predisposing factors are considered using geographic information systems. Ground types at different depths (1 m and 4 m b.g.l.) are used in the statistical susceptibility assessment for the first time. Landslide susceptibility maps are developed using two techniques in presenting landslides, 13 conditioning factors, and three statistical methods: landslide index, weight of evidence, and logistic regression. The considered factors have an influence on mass movement formation, but their roles are different. Many of these passive factors are interrelated and some of them are also related to active factors, i.e. triggers. Consideration of many thematic layers in the statistical approach allows for the selection of the most appropriate geo-environmental variables. The most significant conditioning factors that affect the likelihood of landsliding include land use and land cover as well as topography. The susceptibility maps generated by the index model and many interrelated passive factors appear to be over-predicted. The logistic regression model and only independent controlling factors (slope angle, slope aspect, and lithology) are sufficient to compile a reliable susceptibility map of Gdynia. Prediction rate curve plots show that the susceptibility map produced using logistic regression exhibits the highest prediction accuracy. The results emphasize the need to check independence in the selection of instability factors and the use of an independent subset of landslides for validation.

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Section: Landslide Susceptibility Analysismentioning

confidence: 99%

Landslide susceptibility mapping of Gdynia using geographic information system-based statistical models

Małka

2021

Nat Hazards

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“…This research used bivariate statistics applying the measurement of Weight of Evidence (WOE) model to determine significance factors controlling landslides. The bivariate method assumes that landslide susceptibility factors were not inter-relatedand based on the Bayes' rule [3,10].Weight of Evidence (WOE) is calculated as follows:…”

Section: Methodsmentioning

confidence: 99%

“…This study refers to statistical method aims to determine the factors of landslides in Lima Puluh Kota Regency based on bivariate statistical analysis using the WOE (Weight of Evidence) method to indicate the importance of the presence of the factor for the occurrence of the landslides.The advantage of this method is that its easiness and practicality to apply based on evidence. Researches on this topic have been widely applied in many places or compared with other methods [2][3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

The determinant factors of landslides in Lima puluh kota regency using bivariate statistical analysis 2015-2020

ahyuni

Susetyo

Oktari

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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Regarding disasters, West Sumatra Province have multi-hazard risks. Lima Puluh Kota Regency as a part of the region was frequently hit by floods, landslides, and tornadoes. Especially, almost all of its districts are prone to landslides. The purpose of this study is to analyse the significant factors influencing or controlling landslides in susceptible areas. Eleven natural and human-related factors were supposed to influence were analyzed. This study used bivariate statistical analysis with weight of evidence (WOE) method. The 142 land slides points occurrence from 2015 to 2020 had collected from historical records and field survey. Data divided into 83 train and 59 test data set. The results show there are eight factors that influences landslide occurrence: geology condition, landform, land cover, elevation, density of vegetation greenness, slope, rainfall intensity, and proximity to stream. The other three factors such as soil type, curvature of the slope, and aspect of slope were not taken into account because they are not considered to have a significant influence on landslides.

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“…These studies have been performed on both local and regional scales (e.g. ERENER & DÜZGÜN, 2010;ILAN-LOO, 2011;CHALKIAS et al, 2014;HE & BEIGHLEY, 2008;SHIBIAO et al 2013;PAMELA et al 2018; SHAHABI & HASHIM, 2015, UMAR et al, 2014). There are also some case studies where both of these conditioning param-eters are taken into account when assessing global landslide susceptibility (e.g.…”

Section: The Arbitrary -Polynomial Methodsmentioning

confidence: 99%

Preliminary regional landslide susceptibility assessment using limited data

et al. 2019

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In this paper a heuristic approach for preliminary regional landslide susceptibility assessment using limited amount of data is presented. It is called arbitrary polynomial method and takes into account 5 landslide conditioning parameters: lithology, slope inclination, average annual rainfall, land use and maximum expected seismic intensity. According to the method, in the first stage, a gradation is performed for each of the carefully selected conditioning parameters by assigning so called rating points to the grid cells on which the region is divided. Values of the rating points vary from 0 to 3 and depend on the parameter's character and importance for landslide development within the region of interest. A so called Total Landslide Susceptibility Rating (TLSR) model is obtained by summing the individual rating points of each parameter and dividing the region into five susceptibility zones according to Jenks natural breaks classification. Verification of the TLSR model is then performed by overlaying the landslide inventory map of the selected region over the prepared susceptibility map. The sensitivity of the model can be additionally tested by multiplying the conditioning parameter's rating points by sensitivity coefficients. In this way, additional landslide susceptibility models are obtained, named Weighted Total Landslide Susceptibility Rating (WTLSR) models. As a practical example of the method, two TLSR models are presented here for the Polog region in Republic of Macedonia, for return periods of maximum expected seismic intensity for 100 and 500 years. With over 74% of mapped landslides falling in zones of high and very high susceptibility, the results are considered satisfactory for regional scale landslide modelling and are comparable with more advanced quantitative methods. Additional WTLSR models were prepared, and their correlation identified the best model. The presented approach is considered to be very convenient for conducting preliminary regional landslide susceptibility assessments with the ability to fine-tune the results. Due to its simplicity, it can be applied to additional landslide conditioning parameters other than the one presented in the paper, depending on the region of interest and available data sources. It is especially practical for use in developing countries, where various organizational, technical and economic constraints prevent application of more advanced data driven methods. Limitations and restrictions of the approach are also discussed.

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Weights of Evidence Method for Landslide Susceptibility Mapping in Takengon, Central Aceh, Indonesia

Cited by 24 publications

References 5 publications

Landslide susceptibility mapping of Gdynia using geographic information system-based statistical models

Landslide susceptibility mapping of Gdynia using geographic information system-based statistical models

The determinant factors of landslides in Lima puluh kota regency using bivariate statistical analysis 2015-2020

Preliminary regional landslide susceptibility assessment using limited data

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