Emulator-based global sensitivity analysis for flow-like landslide run-out models

Zhao, Hu; Amann, Florian; Kowalski, Julia

doi:10.1007/s10346-021-01690-w

Cited by 9 publications

(9 citation statements)

References 74 publications

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“…The friction coefficients are rather conceptual than physical (Fischer et al, 2015) and rely on back-analysing past events where field data are available. The calibrated results are often ranges (Zhao et al, 2021) or probability density functions (Quan Luna et al, 2013). In this study, ranges of friction coefficients were chosen as μ = [.02, .3], ξ = [100, 2200] m/s 2 according to Zhao et al (2021).…”

Section: Gaussian Process Emulationmentioning

confidence: 99%

“…The calibrated results are often ranges (Zhao et al, 2021) or probability density functions (Quan Luna et al, 2013). In this study, ranges of friction coefficients were chosen as μ = [.02, .3], ξ = [100, 2200] m/s 2 according to Zhao et al (2021). The release volume of a future landslide event is hardly predictable.…”

Section: Gaussian Process Emulationmentioning

confidence: 99%

“…Such a chain consists of many links, including a digital representation of the topography, the underlying physics-based process model, a numerical solution scheme, the approach to parameter calibration along with the training data it relies on, and concepts used for sensitivity analyses and uncertainty quantification. Challenges in the technical realisation of such integrated workflows have been successfully addressed in the past (Dalbey et al, 2008;Aaron et al, 2019;Sun X. P. et al, 2021b;Zhao et al, 2021;Aaron et al, 2022;Zhao and Kowalski, 2022). It will be of crucial importance in the future to increase the efficiency, sustainability and, hence, acceptance of such orchestrated workflows for landslide risk assessment by improving their robustness, reliability and computational-feasibility.…”

Section: Introductionmentioning

confidence: 99%

“…The surrogate model can be sampled, instead of the sampling from the forward model, and hence the PDF of the quantity of interest can be calculated efficiently. An example of physics-based machine learning techniques proven to be effective in many applications related to geohazards is Gaussian Process Emulation, with successful demonstrations in landslide run-out models (Zhao et al, 2021;Zhao and Kowalski, 2022) and stability of infrastructure slopes (Svalova et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

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Computationally-feasible uncertainty quantification in model-based landslide risk assessment

Yildiz

Zhao²,

Kowalski³

2023

Front. Earth Sci.

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Introduction: Increasing complexity and capacity of computational physics-based landslide run-out modelling yielded highly efficient model-based decision support tools, e.g. landslide susceptibility or run-out maps, or geohazard risk assessments. A reliable, robust and reproducible development of such tools requires a thorough quantification of uncertainties, which are present in every step of computational workflow from input data, such as topography or release zone, to modelling framework used, e.g. numerical error.Methodology: Well-established methods from reliability analysis such as Point Estimate Method (PEM) or Monte Carlo Simulations (MCS) can be used to investigate the uncertainty of model outputs. While PEM requires less computational resources, it does not capture all the details of the uncertain output. MCS tackles this problem, but creates a computational bottleneck. A comparative study is presented herein by conducting multiple forward simulations of landslide run-out for a synthetic and a real-world test case, which are used to construct Gaussian process emulators as a surrogate model to facilitate high-throughput tasks.Results: It was demonstrated that PEM and MCS provide similar expectancies, while the variance and skewness differ, in terms of post-processed scalar outputs, such as impact area or a point-wise flow height. Spatial distribution of the flow height was clearly affected by the choice of method used in uncertainty quantification.Discussion: If only expectancies are to be assessed then one can work with computationally-cheap PEM, yet MCS has to be used when higher order moments are needed. In that case physics-based machine learning techniques, such as Gaussian process emulation, provide strategies to tackle the computational bottleneck. It can be further suggested that computational-feasibility of MCS used in landslide risk assessment can be significantly improved by using surrogate modelling. It should also be noted that the gain in compute time by using Gaussian process emulation critically depends on the computational effort needed to produce the training dataset for emulation by conducting simulations.

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Section: Gaussian Process Emulationmentioning

confidence: 99%

Section: Gaussian Process Emulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Computationally-feasible uncertainty quantification in model-based landslide risk assessment

Yildiz

Zhao²,

Kowalski³

2023

Front. Earth Sci.

Self Cite

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“…A good approximation of the Sobol indices for a high-dimensional problem like the one at hand requires excessive CFD and Vortex Theory model evaluations. Instead, a feedforward neural network was fitted for each objective and the indices were computed on each response surface following the alternative of emulator-based global sensitivity analysis [37,38]. A good convergence was observed when a sample size of 𝑁 = 10 5 was used.…”

Section: Fig 6 Comparison Of Efficiency Between Vortex Theory and 3d ...mentioning

confidence: 99%

Assessment of Multi-fidelity Surrogate Models for High-Altitude Propeller Optimization

Mourousias

Malim

Marinus

et al. 2022

AIAA AVIATION 2022 Forum

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Bayesian active learning for parameter calibration of landslide run-out models

Zhao

Kowalski

2022

Landslides

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Landslide run-out modeling is a powerful model-based decision support tool for landslide hazard assessment and mitigation. Most landslide run-out models contain parameters that cannot be directly measured but rely on back-analysis of past landslide events. As field data on past landslide events come with a certain measurement error, the community developed probabilistic calibration techniques. However, probabilistic parameter calibration of landslide run-out models is often hindered by high computational costs resulting from the long run time of a single simulation and the large number of required model runs. To address this computational challenge, this work proposes an efficient probabilistic parameter calibration method by integrating landslide run-out modeling, Bayesian inference, Gaussian process emulation, and active learning. Here, we present an extensive synthetic case study. The results show that our new method can reduce the number of necessary simulation runs from thousands to a few hundreds owing to Gaussian process emulation and active learning. It is therefore expected to advance the current practice of parameter calibration of landslide run-out models.

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Emulator-based global sensitivity analysis for flow-like landslide run-out models

Cited by 9 publications

References 74 publications

Computationally-feasible uncertainty quantification in model-based landslide risk assessment

Computationally-feasible uncertainty quantification in model-based landslide risk assessment

Assessment of Multi-fidelity Surrogate Models for High-Altitude Propeller Optimization

Bayesian active learning for parameter calibration of landslide run-out models

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