The effect of DEM resolution on the computation of the factor of safety using an infinite slope model

Fuchs, Michael; Torizin, Jewgenij; Kühn, Friedrich

doi:10.1016/j.geomorph.2014.07.015

Cited by 26 publications

(13 citation statements)

References 31 publications

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“…In this section, the influences of the resolution of the digital elevation model (DEM) of the geomorphy [85,86] and the characteristics of the sliding surface, [87] caused by the bias in the field surveys of the geomorphy and sliding surface at the site of the landslide, on the accuracy of the simulation results are studied. Figure 14a shows the influence of the DEM resolution of the geomorphy in the landslide route.…”

Section: Further Discussion On the Other Uncertain Factors In The Probabilistic Analysismentioning

confidence: 99%

Probabilistic analysis of a discrete element modelling of the runout behavior of the Jiweishan landslide

Bing

Gong

Tang

et al. 2021

Num Anal Meth Geomechanics

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Landslide is one of the most destructive geohazards around the world. The destruction of a landslide can be estimated from its deformation and runout behaviors, which might be simulated with numerical software such as particle flow code (PFC). In the PFC simulation of the runout behavior of a landslide, the results are dependent upon the particle‐particle contact micro‐parameters (e.g., contact modulus and friction coefficient). The calibration of the micro‐parameters of the PFC models is a challenge and various uncertainties are involved. The uncertainty in the selection of the modelling parameters leads to uncertainty in the simulated runout behavior of the landslide. As a result, the damage potential of the landslide cannot be accurately evaluated. To this end, this paper presents a probabilistic analysis of the runout behavior of the Jiweishan landslide, in which the uncertainty in the selection of the particle‐particle contact micro‐parameters is explicitly considered. Here, the input micro‐parameters are modeled as discrete random variables, the possible realizations of which are obtained through an orthogonal analysis of the PFC simulation‐based uniaxial compression tests. The derived possible realizations of the micro‐parameters are then adopted as the inputs to the built model of the landslide. With the results obtained from these PFC simulations, the runout behaviors of the Jiweishan landslide, such as the runout distance and the deposit thickness, are studied probabilistically. The results provide an improved estimate of the landslide runout behavior and further aid in making an informed risk assessment of the landslide.

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Section: Further Discussion On the Other Uncertain Factors In The Probabilistic Analysismentioning

confidence: 99%

Probabilistic analysis of a discrete element modelling of the runout behavior of the Jiweishan landslide

Bing

Gong

Tang

et al. 2021

Num Anal Meth Geomechanics

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“…After the elimination, the terrain conditions of the remaining regions was considered as prone to debris flow. Meanwhile, the FS value of the pixel further quantified the likelihood of debris flow occurrence (i.e., the susceptibility) from the aspect of physico-mechanical properties and initiation mechanism [62]. Even if the eliminated regions had a higher FS value but the basic terrain conditions were unsuitable, these regions still did not belong to the debris-flow-prone area.…”

Section: Discussionmentioning

confidence: 99%

Debris Flow Susceptibility Assessment Using the Integrated Random Forest Based Steady-State Infinite Slope Method: A Case Study in Changbai Mountain, China

Alu

Zhang

et al. 2020

Water

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Debris flow events often pose significant damage and are a threat to infrastructure and even livelihoods. Recent studies have mainly focused on determining the susceptibility of debris flow using deterministic or heuristic/probabilistic models. However, each type of model has its own significant advantages with some irreparable disadvantages. The random forest model, which is sensitive to the region where the terrain conditions are suitable for the occurrence of debris flow, was applied along with the steady-state infinite slope method, which is capable of describing the initiation mechanism of debris flow. In this manner, a random-forest-based steady-state infinite slope method was used to conduct susceptibility assessment of debris-flow at Changbai mountain area. Results showed that the assessment accuracy of the proposed random-forest-based steady-state infinite slope method reached 90.88%; however, the accuracy of just the random forest model or steady-state infinite slope method was only 88.48% or 60.45%, respectively. Compared with the single-model assessment results, the assessment accuracy of the proposed method improved by 2.4% and 30.43%, respectively. Meanwhile, the debris-flow-prone area of the proposed method was reduced. The random-forest-based steady-state infinite slope method inherited the excellent diagnostic performance of the random-forest models in the region where the debris flow disaster already occurred; meanwhile, this method further refined the debris-flow-prone area from the suitable terrain area based on physico-mechanical properties; thus, the performance of this method was better than those of the other two models.

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“…One of the most significant variables affecting landslide susceptibility, apart from geology, weather conditions, and land cover, is topography (i.e., slope angle, aspect, and curvature) [46]. Therefore, the selection of a proper digital elevation model (DEM) is essential in LS assessment [47].…”

Section: Introductionmentioning

confidence: 99%

Earth Observation and GIS-Based Analysis for Landslide Susceptibility and Risk Assessment

Psomiadis

Charizopoulos

Efthimiou

et al. 2020

IJGI

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Landslides can cause severe problems to the social and economic well-being. In order to effectively mitigate landslide hazards, the development of detailed susceptibility maps is required, towards implementing targeted risk management plans. This study aims to create detailed landslide susceptibility (LS) and landslide risk (LR) maps of the Sperchios River basin by applying an expert semi-quantitative approach that integrates the Geographic Information Systems (GIS)-based multicriteria analysis and Earth Observation (EO) data. Adopting the analytic hierarchy process (AHP) for a weighted linear combination (WLC) approach, eleven evaluation parameters were selected. The results were validated using a historic landslide database, enriched with new landslide locations mapped by satellite and aerial imagery interpretation and field surveys. Moreover, the landslide risk map of the area was also developed, based on the LS delineation, considering additionally the anthropogenic exposure and overall vulnerability of the area. The results showed that the most susceptible areas are located at the west and south-west regions of the basin. The synergistic use of GIS-based analysis and EO data can provide a useful tool for the design of natural hazards prevention policy at highly susceptible to risk landslide risk areas.

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The effect of DEM resolution on the computation of the factor of safety using an infinite slope model

Cited by 26 publications

References 31 publications

Probabilistic analysis of a discrete element modelling of the runout behavior of the Jiweishan landslide

Probabilistic analysis of a discrete element modelling of the runout behavior of the Jiweishan landslide

Debris Flow Susceptibility Assessment Using the Integrated Random Forest Based Steady-State Infinite Slope Method: A Case Study in Changbai Mountain, China

Earth Observation and GIS-Based Analysis for Landslide Susceptibility and Risk Assessment

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