Uncertainty and Reliability Analysis Applied to Slope Stability: A Case Study From Sungun Copper Mine

Abbaszadeh, Mohsen; Shahriar, K.; Sharifzadeh, Mostafa; Heydari, Mehrdad

doi:10.1007/s10706-011-9405-1

Cited by 31 publications

(24 citation statements)

References 17 publications

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“…The LandslideProbability component is written in Python and implemented with a model driver (written as a Jupyter Notebook) using the workflow shown in Fig. 1 of the component's user manual (Strauch et al 2018 (Malkawi et al, 2000) to > 1200 (Abbaszadeh et al, 2011) was found to be sufficient in the literature. We used 3000 in this study.…”

Section: Model Development In Landlabmentioning

confidence: 99%

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A hydroclimatological approach to predicting regional landslide probability using Landlab

et al. 2018

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Abstract. We develop a hydroclimatological approach to the modeling of regional shallow landslide initiation that integrates spatial and temporal dimensions of parameter uncertainty to estimate an annual probability of landslide initiation based on Monte Carlo simulations. The physically based model couples the infinite-slope stability model with a steady-state subsurface flow representation and operates in a digital elevation model. Spatially distributed gridded data for soil properties and vegetation classification are used for parameter estimation of probability distributions that characterize model input uncertainty. Hydrologic forcing to the model is through annual maximum daily recharge to subsurface flow obtained from a macroscale hydrologic model. We demonstrate the model in a steep mountainous region in northern Washington, USA, over 2700 km 2 . The influence of soil depth on the probability of landslide initiation is investigated through comparisons among model output produced using three different soil depth scenarios reflecting the uncertainty of soil depth and its potential long-term variability. We found elevation-dependent patterns in probability of landslide initiation that showed the stabilizing effects of forests at low elevations, an increased landslide probability with forest decline at midelevations (1400 to 2400 m), and soil limitation and steep topographic controls at high alpine elevations and in post-glacial landscapes. These dominant controls manifest themselves in a bimodal distribution of spatial annual landslide probability. Model testing with limited observations revealed similarly moderate model confidence for the three hazard maps, suggesting suitable use as relative hazard products. The model is available as a component in Landlab, an open-source, Python-based landscape earth systems modeling environment, and is designed to be easily reproduced utilizing HydroShare cyberinfrastructure.

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Section: Model Development In Landlabmentioning

confidence: 99%

“…• in other studies (e.g., Abbaszadeh et al, 2011;Arnone et al, 2016a). Other spatial soil and vegetation datasets can be used in the preprocessing of the model.…”

Section: Model Drivermentioning

confidence: 99%

A hydroclimatological approach to predicting regional landslide probability using Landlab

et al. 2018

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“…Once the component has been instantiated, the component's method calculate_landslide_probability() is executed in a for loop that performs the calculations at each node. An iteration number in the range of 700 (Malkawi et al, 2000) to > 1200 (Abbaszadeh et al, 2011) was found to be sufficient in the literature. We used 3000 in this study.…”

Section: Model Development In Landlabmentioning

confidence: 92%

“…3.2.1 for details). The current model design assumes negligible correlation between C and ∅ as assumed in other studies (e.g., Abbaszadeh et al, 2011;Arnone et al, 2016a). Other spatial soil and vegetation datasets can be used in the preprocessing of the model.…”

Section: Model Development In Landlabmentioning

confidence: 99%

A hydro-climatological approach to predicting regional landslide probability using Landlab

Strauch¹,

Istanbulluoglu²,

Nudurupati³

et al. 2017

Preprint

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We develop a hydroclimatological approach to the modeling of regional shallow landslide initiation that integrates spatial and temporal dimensions of parameter uncertainty to estimate an annual probability of landslide initiation based on Monte Carlo simulations. The physically based model couples the infinite-slope stability model with a steady-state subsurface flow representation and operates in a digital elevation model. Spatially distributed gridded data for soil properties and vegetation classification are used for parameter estimation of probability distributions that characterize model input uncertainty. Hydrologic forcing to the model is through annual maximum daily recharge to subsurface flow obtained from a macroscale hydrologic model. We demonstrate the model in a steep mountainous region in northern Washington, USA, over 2700 km 2 . The influence of soil depth on the probability of landslide initiation is investigated through comparisons among model output produced using three different soil depth scenarios reflecting the uncertainty of soil depth and its potential long-term variability. We found elevation-dependent patterns in probability of landslide initiation that showed the stabilizing effects of forests at low elevations, an increased landslide probability with forest decline at midelevations (1400 to 2400 m), and soil limitation and steep topographic controls at high alpine elevations and in post-glacial landscapes. These dominant controls manifest themselves in a bimodal distribution of spatial annual landslide probability. Model testing with limited observations revealed similarly moderate model confidence for the three hazard maps, suggesting suitable use as relative hazard products. The model is available as a component in Landlab, an open-source, Python-based landscape earth systems modeling environment, and is designed to be easily reproduced utilizing HydroShare cyberinfrastructure. Dunne, 1997a, b) and ecosystem dynamics (Pollock, 1998;May et al., 2009). Landslide hazard maps are a common tool used to characterize the relative potential for landslide occurrence in space, either qualitatively (using susceptibility levels) or quantitatively (using modeled landslide probabilities) (van Westen et al., 2006;Raia et al., 2014).

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“…New attempts have been made to apply the PEM [12,13] and by FOSM method [14,15] in slope stability. Recent researches have been made to apply FORM method in slope stability [16].…”

Section: Methodsmentioning

confidence: 99%

Analytical stochastic analysis of seismic stability of infinite slope

Johari

Khodaparast

2015

Soil Dynamics and Earthquake Engineering

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Uncertainty and Reliability Analysis Applied to Slope Stability: A Case Study From Sungun Copper Mine

Cited by 31 publications

References 17 publications

A hydroclimatological approach to predicting regional landslide probability using Landlab

A hydroclimatological approach to predicting regional landslide probability using Landlab

A hydro-climatological approach to predicting regional landslide probability using Landlab

Analytical stochastic analysis of seismic stability of infinite slope

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