Modeling of landslide volume estimation

Amirahmadi, Abolghasem; Pourhashemi, Sima; Karami, Mahmoud; Akbari, Ebrahim

doi:10.1515/geo-2016-0032

Cited by 8 publications

(5 citation statements)

References 33 publications

(57 reference statements)

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“…Eq. ( 2) proposed to estimate the volume of landslides that occurred based on the landslide area with a determination coefficient value of 0,99 and can be used in any region because the data sources come from various regions of different countries (Amirahmadi et al, 2016).…”

Section: Table 1 Interpretation Of the Coefficient Of Determinationmentioning

confidence: 99%

An Empirical-Statistical Model for Landslide Runout Distance Prediction in Indonesia

Apriani

Credidi

Khala

2022

pondasi

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There have been many attempts and methods for predicting landslide-affected areas; empirical methods, numerical methods, and laboratory models are commonly used for prediction. Laboratory and numerical models require an input of parameters that are difficult to determine accurately. At the same time, empirical statistical methods use statistical methods based on historical data of landslide events to form an empirical model. Statistical analysis of empirical observations builds a possible relationship between disaster area characteristics and slide behavior because it does not require detailed mechanics of avalanche movement; the empirical-statistical model is a simple and practical tool in the initial assessment to predict the sliding distance of an avalanche that will occur. The main discussion of this study is that the volume of avalanches (V) has a more significant influence than the height of the slope (H) on the length of the avalanche (L) that occurs. Fifty-nine data on landslide events that have occurred in Indonesia are used to a prediction model for landslide events reviewing the slope geometry parameters in the form of H, slope (θ), and V and discussing the main factors that affect the sliding distance of avalanches that have not been discussed in research in the Indonesian territory. The analysis shows that H has a significant effect on the sliding distance of the avalanche compared to V. The best model produced to predict the sliding distance of the avalanche is L = 6.918 H0,840 and produces an average error rate of 29% for the landslide measurement data.

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Section: Table 1 Interpretation Of the Coefficient Of Determinationmentioning

confidence: 99%

An Empirical-Statistical Model for Landslide Runout Distance Prediction in Indonesia

Apriani

Credidi

Khala

2022

pondasi

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“…When the slope is long and steep enough it is only the volume that limits the runout length. The total volume, V S , must be estimated from topographical maps [45], easy enough after the slide, but may be very difficult for suspectable sites where there have been no slides yet. For a translatory wave of length L VS from the point where the depth is y VS to the beginning of the wave where y = 0, V S = 0 L VS ydx ∼ = y VS L VS (12) As the front of the translatory wave is very steep, Equation 12is an expression that is usually accurate enough for the value of the integral.…”

Section: Translatory Wave Theory Used On Landslide Flowmentioning

confidence: 99%

Physics and Modeling of Various Hazardous Landslides

Eliasson¹,

Sæmundsson²

2021

Geosciences

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In 2014, the Varnes classification system for landslides was updated. Complex landslides can still be a problem to classify as the classification does not include the flow type in the hydrodynamical sense. Three examples of Icelandic landslides are presented and later used as case studies in order to demonstrate the methods suggested to analyze the flow. The methods are based on the different physical properties of the flow types of the slides. Three different flow types are presented, named type (i), (ii), and (iii). Types (i) and (ii) do not include turbulent flows and their flow paths are sometimes independent of the velocity. Type (iii) include high velocity flows; they are treated with the translator wave theory, where a new type of a slope factor is used. It allows the slide to stop when the slope has flattened out to the value that corresponds to the stable slope property of the flowing material. The type studies are for a fast slide of this type, also a large slip circle slide that turns into a fast-flowing slide farther down the path and finally a large slide running so fast that it can run for a kilometer on flat land where it stops with a steep front.

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“…While the first one can provide relative information on landslide susceptibility, it lacks the capability to predict the frequency and spatially distributed hazard intensity, which are needed for proper hazard and risk assessment. Additionally, physically-based modelling can allow for more detailed investigation of physical processes, and for analyzing potential future scenarios (Guzzetti et al, 2009;Amirahmadi et al, 2016). Direct downsides are the computational costs (Hammah et al, 2010) and complexities of parameterization (Reder et al, 2018).…”

Section: Introductionmentioning

confidence: 99%