Landslide susceptibility assessment of the Kraľovany–Liptovský Mikuláš railway case study

Bednařík, Martin; Magulova, Barbora; Matys, M.; Marschalko, Marián

doi:10.1016/j.pce.2009.12.002

Cited by 175 publications

(79 citation statements)

References 11 publications

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“…Such information would provide a crucial reference for researchers and decision makers in industries like insurance for and project managers in some nongovernmental organizations (NGO). For international and national insurance or reinsurance companies, such a map will provide them with clear knowledge of landslide hotspots at a macro level, which will help them concentrate on those susceptible areas and form relevant marketing strategies like transferring risks (Bednarik et al, 2010). Geographers could also find it useful for revealing spatial patterns of landslide distribution.…”

Section: Introductionmentioning

confidence: 99%

Landslide susceptibility mapping on a global scale using the method of logistic regression

Lin

Wang

2017

Nat. Hazards Earth Syst. Sci.

121

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Abstract. This paper proposes a statistical model for mapping global landslide susceptibility based on logistic regression. After investigating explanatory factors for landslides in the existing literature, five factors were selected for model landslide susceptibility: relative relief, extreme precipitation, lithology, ground motion and soil moisture. When building the model, 70 % of landslide and nonlandslide points were randomly selected for logistic regression, and the others were used for model validation. To evaluate the accuracy of predictive models, this paper adopts several criteria including a receiver operating characteristic (ROC) curve method. Logistic regression experiments found all five factors to be significant in explaining landslide occurrence on a global scale. During the modeling process, percentage correct in confusion matrix of landslide classification was approximately 80 % and the area under the curve (AUC) was nearly 0.87. During the validation process, the above statistics were about 81 % and 0.88, respectively. Such a result indicates that the model has strong robustness and stable performance. This model found that at a global scale, soil moisture can be dominant in the occurrence of landslides and topographic factor may be secondary.

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Section: Introductionmentioning

confidence: 99%

Landslide susceptibility mapping on a global scale using the method of logistic regression

Lin

Wang

2017

Nat. Hazards Earth Syst. Sci.

121

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“…Landslide hazards are one of the major life threats resulting from 39 earthquakes, flooding and storm events in mountainous areas (Brabb, 1991;Brabb, 1993;Marano et 40 al., 2010;Suzen and Kaya, 2011). Due to the interaction with other hazards and the spatially dispersed 41 nature of landslide occurrences, it is necessary to map susceptibility to failure especially in areas with 42 elements at risk (Bednarik et al, 2010). Landslide susceptibility can be mapped by fitting a statistical 43 model to data on historical landslide occurrence and a set of covariates (Brabb, 1984;Hansen, 1984; 44…”

Section: Introduction 33mentioning

confidence: 99%

A systematic review of landslide probability mapping using logistic regression

2015

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11Logistic regression studies which assess landslide susceptibility are widely available in the literature. 12However, a global review of these studies to synthesise and compare the results does not exist. There 13 are currently no guidelines for selection of covariates to be used in logistic regression analysis and as 14 such, the covariates selected vary widely between studies. An inventory of significant covariates 15 associated with landsliding produced from the full set of such studies globally would be a useful aid to 16 the selection of covariates in future logistic regression studies. Thus, studies using logistic regression 17 for landslide susceptibility estimation published in the literature were collated and a database created 18 of the significant factors affecting the generation of landslides. The database records the paper the 19 data were taken from, the year of publication, the approximate longitude and latitude of the study 20 area, the trigger method (where appropriate), and the most dominant type of landslides occurring in 21 the study area. The significant and non-significant (at the 95% confidence level) covariates were 22 recorded, as well as their coefficient, statistical significance, and unit of measurement. The most 23 common statistically significant covariate used in landslide logistic regression was slope, followed by 24 aspect. The significant covariates related to landsliding varied for earthquake-induced landslides 25 compared to rainfall-induced landslides, and between landslide type. More importantly, the full range 26 of covariates used was identified along with their frequencies of inclusion. The analysis showed that 27 2 there needs to be more clarity and consistency in the methodology for selecting covariates for logistic 28 regression analysis and in the metrics included when presenting the results. Several recommendations 29 for future studies were given. 30 31

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“…The natural break classification scheme was applied for the classification of the landslide susceptibility zones. Recently, the classification method mainly used the natural break classification scheme [72][73][74][75][76]. The landslide susceptibility maps were reclassified into four classes: low (0-0.3323), moderate (0.3323-0.4593), high (0.4593-0.5699), and very high (0.5699-0.8533).…”

Section: Landslides Susceptibility Mappingmentioning

confidence: 99%

Landslide Susceptibility Mapping in Vertical Distribution Law of Precipitation Area: Case of the Xulong Hydropower Station Reservoir, Southwestern China

Cao

Wang

Chen

et al. 2016

Water

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This study focused on landslide susceptibility analysis mapping of the Xulong hydropower station reservoir, which is located in the upstream of Jinsha River, a rapidly uplifting region of the Tibetan Plateau region. Nine factors were employed as landslide conditioning factors in landslide susceptibility mapping. These factors included the slope angle, slope aspect, curvature, geology, distance-to-fault, distance-to-river, vegetation, bedrock uplift and annual precipitation. The rapid bedrock uplift factor was represented by the slope angle. The eight factors were processed with the information content model. Since this area has a significant vertical distribution law of precipitation, the annual precipitation factor was analyzed separately. The analytic hierarchy process weighting method was used to calculate the weights of nine factors. Thus, this study proposed a component approach to combine the normalized eight-factor results with the normalized annual precipitation distribution results. Subsequently, the results were plotted in geographic information system (GIS) and a landslide susceptibility map was produced. The evaluation accuracy analysis method was used as a validation approach. The landslide susceptibility classes were divided into four classes, including low, moderate, high and very high. The results show that the four susceptibility class ratios are 12.9%, 35.06%, 34.11%and 17.92% of the study area, respectively. The red belt in the high elevation area represents the very high susceptibility zones, which followed the vertical distribution law of precipitation. The prediction accuracy was 85.74%, which meant that the susceptibility map was confirmed to be reliable and reasonable. This susceptibility map may contribute to averting the landslide risk in the future construction of the Xulong hydropower station.

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Landslide susceptibility assessment of the Kraľovany–Liptovský Mikuláš railway case study

Cited by 175 publications

References 11 publications

Landslide susceptibility mapping on a global scale using the method of logistic regression

Landslide susceptibility mapping on a global scale using the method of logistic regression

A systematic review of landslide probability mapping using logistic regression

Landslide Susceptibility Mapping in Vertical Distribution Law of Precipitation Area: Case of the Xulong Hydropower Station Reservoir, Southwestern China

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