Emulating computer experiments of transport infrastructure slope stability using Gaussian processes and Bayesian inference

Svalova, Aleksandra; Helm, P.; Prangle, Dennis; Rouainia, Mohamed; Glendinning, S. G.; Wilkinson, Darren J.

doi:10.1017/dce.2021.14

Cited by 10 publications

(3 citation statements)

References 48 publications

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“…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%

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: Introductionmentioning

confidence: 99%

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

Yildiz

Zhao²,

Kowalski³

2023

Front. Earth Sci.

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“…The editors and publisher of Data-Centric Engineering have awarded the Open Data and Open Materials badges to this article Svalova A, et al (2021).…”

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confidence: 99%

Emulating computer experiments of transport infrastructure slope stability using Gaussian processes and Bayesian inference—ADDENDUM

Svalova¹,

Helm²,

Prangle³

et al. 2022

DCE

Self Cite

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“…Conditioned on a set of computer model evaluations, a GP emulator is used to generate predictions together with a measure of uncertainty about the predicted model output at inputs that have not been tested. GP emulators have been widely used in climate and environmental studies (Conti et al, 2009;Williamson and Blaker, 2014;Volodina and Williamson, 2020), energy electricity prices (Wilson, Goldstein and Dent, 2022) and transport infrastructure (Svalova et al, 2021).…”

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confidence: 99%

A Bayesian Decision Support System in Energy Systems Planning

Volodina¹,

Sonenberg²,

Challenor³

et al. 2022

Preprint

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Gaussian Process (GP) emulators are widely used to approximate complex computer model behaviour across the input space. Motivated by the problem of coupling computer models, recently progress has been made in the theory of the analysis of networks of connected GP emulators. In this paper, we combine these recent methodological advances with classical state-space models to construct a Bayesian decision support system. This approach gives a coherent probability model that produces predictions with the measure of uncertainty in terms of two first moments and enables the propagation of uncertainty from individual decision components.This methodology is used to produce a decision support tool for a UK county council considering low carbon technologies to transform its infrastructure to reach a net-zero carbon target. In particular, we demonstrate how to couple information from an energy model, a heating demand model, and gas and electricity price time-series to quantitatively assess the impact on operational costs of various policy choices and changes in the energy market.

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Emulating computer experiments of transport infrastructure slope stability using Gaussian processes and Bayesian inference

Cited by 10 publications

References 48 publications

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

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

Emulating computer experiments of transport infrastructure slope stability using Gaussian processes and Bayesian inference—ADDENDUM

A Bayesian Decision Support System in Energy Systems Planning

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