Landslide susceptibility: a statistically-based assessment on a depositional pyroclastic ramp

Murillo-García, Franny G.; Steger, Stefan; Ayala, Irasema Alcántara

doi:10.1007/s11629-018-5225-6

Cited by 20 publications

(10 citation statements)

References 67 publications

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“…Although independence tests of layers are important, they are not considered in many studies (e.g. Devkota et al 2013;Murillo-García et. al.…”

Section: Discussionmentioning

confidence: 99%

“…The approaches and methods for assessing landslide susceptibility can be grouped into five broad categories: geomorphological mapping, analysis of landslide inventories, heuristic or index-based approaches, process-based methods, and statistical methods (Reichenbach et al 2018). Physical and statistical methods are preferred to ascertain landslide susceptibility in quantitative terms (Liu and Duan 2018;Hemasinghe et al 2018;Murillo-García et al 2019). According to Reichenbach et al (2018), statistical landslide index (SLI), weight of evidence (WOE), and logistic regression (LR) models are the most common statistical landslide susceptibility models.…”

Section: Introductionmentioning

confidence: 99%

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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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“…Although independence tests of layers are important, they are not considered in many studies (e.g. Devkota et al 2013;Murillo-García et. al.…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Małka

2021

Nat Hazards

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“…Previous researchers have applied bivariate and multivariate statistical methods and compared their performance in LSM 46,47 . Although CF, FR and IV have similarity in both principles and results but their performance varies when combined with LR model.…”

Section: Discussionmentioning

confidence: 99%

Application of an Integrated Model Based on Bivariate and Multivariate Method in Landslide Susceptibility Mapping

Wang

Zhu

2020

Preprint

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Landslides usually result in human losses and economic damages in mountainous areas especially for Himalayan areas. Landslide susceptibility mapping (LSM) is a key approach for avoiding hazard and risk. This study aims to explore an improved model combining multivariate and bivariate statistical methods for LSM. Four models were established as logistic regression (LR), LR integrated with certain factor (CF), LR integrated with frequency ratio (FR) and LR integrated with information value method (IV) and their performance was compared in LSM. Firstly, a landslide inventory map with 313 determined landslide events was prepared and 12 predisposing factors were selected. Secondly, the dataset was randomly divided into two parts, 75% of which was used for modeling and 25% for validation. Finally, area under the curve (AUC) and statistical metrics were applied to validate and compare the performance of the models. Results show that the performance of IVLR model is the best (AUC 0.792 and accuracy=78.8%). Besides, the LSM constructed by IVLR model did a reasonable job at predicting the distribution of susceptible areas. It identified the major factors and intervals of high susceptibility that profile curvature greater than 0.1, less than 2 km from the stream, maximum elevation difference greater than 1200 m and rainfall between 440 and 450 mm were prone to landslide. The conclusion reveals that the quality of LSM can be improved by comparing and combining the bivariate and multivariate methods, which serve as a more effective guide for land use planning in the study area or other highlands where landslides are frequent.

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“…Conditional frequency plots (for continuous variables) and spineplots (for categorical variables) were used to highlight the ratio of landslide presence to absence across the geo-environmental data values. First insights into the capability of geo-environmental variables to distinguish landslide presence from absence observations were gained by evaluating the discriminatory power of single-variable models (Murillo-García et al, 2019;Steger et al, 2020). In this case, the obtained metric reflects the fitting performance of a single-predictor logistic regression measured via the area under the receiver operating characteristic curve (AUROC) (Beguería, 2006;Remondo et al, 2003).…”

Section: Exploratory Data Analysismentioning

confidence: 99%

Counteracting flawed landslide data in statistically based landslide susceptibility modelling for very large areas: a national-scale assessment for Austria

2021

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The reliability of input data to be used within statistically based landslide susceptibility models usually determines the quality of the resulting maps. For very large territories, landslide susceptibility assessments are commonly built upon spatially incomplete and positionally inaccurate landslide information. The unavailability of flawless input data is contrasted by the need to identify landslide-prone terrain at such spatial scales. Instead of simply ignoring errors in the landslide data, we argue that modellers have to explicitly adopt their modelling design to avoid misleading results. This study examined different modelling strategies to reduce undesirable effects of error-prone landslide inventory data, namely systematic spatial incompleteness and positional inaccuracies. For this purpose, the Austrian territory with its abundant but heterogeneous landslide data was selected as a study site. Conventional modelling practices were compared with alternative modelling designs to elucidate whether an active counterbalancing of flawed landslide information can improve the modelling results. In this context, we compared widely applied logistic regression with an approach that allows minimizing the effects of heterogeneously complete landslide information (i.e. mixed-effects logistic regression). The challenge of positionally inaccurate landslide samples was tackled by elaborating and comparing the models for different terrain representations, namely grid cells, and slope units. The results showed that conventional logistic regression tended to reproduce incompleteness inherent in landslide training data in case the underlying model relied on explanatory variables directly related to the data bias. The adoption of a mixed-effects modelling approach appeared to reduce these undesired effects and led to geomorphologically more coherent spatial predictions. As a consequence of their larger spatial extent, the slope unit–based models were able to better cope with positional inaccuracies of the landslide data compared to their grid-based equals. The presented research demonstrates that in the context of very large area susceptibility modelling (i) ignoring flaws in available landslide data can lead to geomorphically incoherent results despite an apparent high statistical performance and that (ii) landslide data imperfections can actively be diminished by adjusting the research design according to the respective input data imperfections.

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Landslide susceptibility: a statistically-based assessment on a depositional pyroclastic ramp

Cited by 20 publications

References 67 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

Application of an Integrated Model Based on Bivariate and Multivariate Method in Landslide Susceptibility Mapping

Counteracting flawed landslide data in statistically based landslide susceptibility modelling for very large areas: a national-scale assessment for Austria

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