Bayeslands: A Bayesian inference approach for parameter uncertainty quantification in Badlands

Chandra, Rohitash; Azam, Danial; Müller, R. Dietmar; Salles, Tristan; Cripps, Sally

doi:10.1016/j.cageo.2019.06.012

Cited by 22 publications

(38 citation statements)

References 55 publications

(76 reference statements)

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“…Figure shows an example of the posterior distributions for precipitation and erodibility, while fixing all other parameters for the CM problem. We note that our previous work (Chandra et al., ) showed that different combinations of precipitation and erodibility gave rise to visually indistinguishable topography for both the Cr and CM problem. Figure has a definite global maximum, but also several local maxima.…”

Section: Background and Related Workmentioning

confidence: 51%

“…Note that the results for SC‐Bayeslands are taken from Chandra et al. () with 5% burn‐in, while PT‐Bayeslands features 25% burn‐in.…”

Section: Resultsmentioning

confidence: 99%

“…The initial and final topographies for all three problems are shown in Figure . We note that Cr and CM have been adapted from examples of the Badlands model (Salles & Hardiman, ; Salles, ; Salles et al., ) and also used in our previous work (Chandra et al., ).…”

Section: Methodsmentioning

confidence: 99%

“…The first is by comparing the posterior distribution of the parameters with those used to generate the ground‐truth data. The second is by comparing the computational time for the PT‐Bayeslands versus SC‐Bayeslands from earlier work (Chandra et al., ). The third is by comparing the predicted/simulated Badlands landscape with the ground‐truth using the root‐mean‐square error (RMSE).…”

Section: Methodsmentioning

confidence: 99%

“…Log posterior (likelihood) surface of the continental margin problem as function of precipitation, ρ , and erodibility, ϵ . The true values have a clear peak in the log‐posterior (precipitation = 1.5 m/a, erodibility = 5 e −6 ); however, many suboptimal peaks (marked in red) for this parameter pair exist (Chandra et al., ).…”

Section: Background and Related Workmentioning

confidence: 99%

See 4 more Smart Citations

Multicore Parallel Tempering Bayeslands for Basin and Landscape Evolution

Chandra

Müller

Azam

et al. 2019

Geochem Geophys Geosyst

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The Bayesian paradigm is becoming an increasingly popular framework for estimation and uncertainty quantification of unknown parameters in geophysical inversion problems. Badlands is a landscape evolution model for simulating topography evolution at a broad range of spatial and temporal scales. Our previous work presented Bayeslands that used the Bayesian inference for estimating unknown parameters in the Badlands model using Markov chain Monte Carlo sampling. Bayeslands faced challenges in terms of computational issues and convergence due to multimodal posterior distributions. Parallel tempering is an advanced Markov chain Monte Carlo method suited for irregular and multimodal posterior distributions. In this paper, we extend Bayeslands using parallel tempering with high‐performance computing to address previous limitations in Bayeslands. Our results show that parallel tempering Bayeslands not only reduces the computation time‚ but also provides an improvement in sampling multimodal posterior distributions, which motivates future application to continental scale landscape evolution models.

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Section: Background and Related Workmentioning

confidence: 51%

“…Note that the results for SC‐Bayeslands are taken from Chandra et al. () with 5% burn‐in, while PT‐Bayeslands features 25% burn‐in.…”

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

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Multicore Parallel Tempering Bayeslands for Basin and Landscape Evolution

Chandra

Müller

Azam

et al. 2019

Geochem Geophys Geosyst

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The impact of different rainfall products on landscape modelling simulations

Skinner

Peleg

Quinn

et al. 2020

Earth Surf Processes Landf

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Rainfall products can contain significantly different spatiotemporal estimates, depending on their underlying data and final constructed resolution. Commonly used products, such as rain gauges, rain gauge networks, and weather radar, differ in their information content regarding intensities, spatial variability, and natural climatic variability, therefore producing different estimates. Landscape evolution models (LEMs) simulate the geomorphic changes in landscapes, and current models can simulate timeframes from event level to millions of years and some use rainfall inputs to drive them. However, the impact of different rainfall products on LEM outputs has never been considered. This study uses the STREAP rainfall generator, calibrated using commonly used rainfall observation products, to produce longer rainfall records than the observations to drive the CAESAR‐Lisflood LEM to examine how differences in rainfall products affect simulated landscapes. The results show that the simulation of changes to basin geomorphology is sensitive to the differences between rainfall products, with these differences expressed linearly in discharges but non‐linearly in sediment yields. Furthermore, when applied over a 1500‐year period, large differences in the simulated long profiles were observed, with the simulations producing greater sediment yields showing erosion extending further downstream. This suggests that the choice of rainfall product to drive LEMs has a large impact on the final simulated landscapes. The combination of rainfall generator model and LEMs represents a potentially powerful method for assessing the impacts of rainfall product differences on landscapes and their short‐ and long‐term evolution. © 2020 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd

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Expedited surrogate-based quantification of engineering tolerances using a modified polynomial regression

Parnianifard

Chaudhary

Mumtaz

et al. 2023

Struct Multidisc Optim

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Statistical analysis is frequently used to determine how manufacturing tolerances or operating condition uncertainties affect system performance. Surrogate is one of the accelerating ways in engineering tolerance quantification to analyze uncertainty with an acceptable computational burden rather than costly traditional methods such as Monte Carlo simulation. Compared with more complicated surrogates such as the Gaussian process, or Radial Basis Function (RBF), the Polynomial Regression (PR) provides simpler formulations yet acceptable outcomes. However, PR with the common least-squares method needs to be more accurate and flexible for approximating nonlinear and nonconvex models. In this study, a new approach is proposed to enhance the accuracy and approximation power of PR in dealing with uncertainty quantification in engineering tolerances. For this purpose, first, by computing the differences between training sample points and a reference point (e.g., nominal design), we employ certain linear and exponential basis functions to transform an original variable design into new transformed variables. A second adjustment is made to calculate the bias between the true simulation model and the surrogate’s approximated response. To demonstrate the effectiveness of the proposed PR approach, we provide comparison results between conventional and proposed surrogates employing four practical problems with geometric fabrication tolerances such as three-bar truss design, welded beam design, and trajectory planning of two-link and three-link (two and three degrees of freedom) robot manipulator. The obtained results prove the preference of the proposed approach over conventional PR by improving the approximation accuracy of the model with significantly lower prediction errors.

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Bayeslands: A Bayesian inference approach for parameter uncertainty quantification in Badlands

Cited by 22 publications

References 55 publications

Multicore Parallel Tempering Bayeslands for Basin and Landscape Evolution

Multicore Parallel Tempering Bayeslands for Basin and Landscape Evolution

The impact of different rainfall products on landscape modelling simulations

Expedited surrogate-based quantification of engineering tolerances using a modified polynomial regression

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