Review and comparison of two meta-model-based uncertainty propagation analysis methods in groundwater applications: polynomial chaos expansion and Gaussian process emulation

Rajabi, Mohammad Mahdi

doi:10.1007/s00477-018-1637-7

Cited by 32 publications

(17 citation statements)

References 85 publications

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“…If the classification results were used in other tasks, these uncertainties would influence the consequent analysis. Accordingly, it is necessary to provide uncertainty analysis in real life applications using, for example, uncertainty propagation methods (Rajabi 2019, Chen et al 2019. To alleviate any such influence on the evaluation of LPPT, the training data and validation data both used the assumed true categories.…”

Section: Recognition Of Vegetation Types From Remotely Sensed Imagerymentioning

confidence: 99%

Incorporating spatial association into statistical classifiers: local pattern-based prior tuning

Bai

Cao

Atkinson

et al. 2020

International Journal of Geographical Information Science

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This paper proposes a new classification method for spatial data by adjusting prior class probabilities according to local spatial patterns. First, the proposed method uses a classical statistical classifier to model training data. Second, the prior class probabilities are estimated according to the local spatial pattern and the classifier for each unseen object is adapted using the estimated prior probability. Finally, each unseen object is classified using its adapted classifier. Because the new method can be coupled with both generative and discriminant statistical classifiers, it performs generally more accurately than other methods for a variety of different spatial datasets. Experimental results show that this method has a lower prediction error than statistical classifiers that take no spatial information into account. Moreover, in the experiments, the new method also outperforms spatial auto-logistic regression and Markov random field-based methods when an appropriate estimate of local prior class distribution is used.

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Section: Recognition Of Vegetation Types From Remotely Sensed Imagerymentioning

confidence: 99%

Incorporating spatial association into statistical classifiers: local pattern-based prior tuning

Bai

Cao

Atkinson

et al. 2020

International Journal of Geographical Information Science

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“…An increasingly popular approach to decrease the computational effort of complex models is using surrogate models. Recent comprehensive reviews were given by Ratto et al (2012), who considered environmental models, Razavi et al (2012b), who considered hydrological models, and Asher et al (2015) and Rajabi (2019), who both considered groundwater models. A surrogate-model, also known as a meta-model, proxy-model, emulator-model, or low-fidelity model, is in its most general form a simpler representation of a complex model.…”

Section: Introductionmentioning

confidence: 99%

Sampling behavioral model parameters for ensemble-based sensitivity analysis using Gaussian process emulation and active subspaces

Erdal

Xiao

Nowak

et al. 2020

Stoch Environ Res Risk Assess

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Ensemble-based uncertainty quantification and global sensitivity analysis of environmental models requires generating large ensembles of parameter-sets. This can already be difficult when analyzing moderately complex models based on partial differential equations because many parameter combinations cause an implausible model behavior even though the individual parameters are within plausible ranges. In this work, we apply Gaussian Process Emulators (GPE) as surrogate models in a sampling scheme. In an active-training phase of the surrogate model, we target the behavioral boundary of the parameter space before sampling this behavioral part of the parameter space more evenly by passive sampling. Active learning increases the subsequent sampling efficiency, but its additional costs pay off only for a sufficiently large sample size. We exemplify our idea with a catchment-scale subsurface flow model with uncertain material properties, boundary conditions, and geometric descriptors of the geological structure. We then perform a global-sensitivity analysis of the resulting behavioral dataset using the active-subspace method, which requires approximating the local sensitivities of the target quantity with respect to all parameters at all sampled locations in parameter space. The Gaussian Process Emulator implicitly provides an analytical expression for this gradient, thus improving the accuracy of the active-subspace construction. When applying the GPE-based preselection, 70–90% of the samples were confirmed to be behavioral by running the full model, whereas only 0.5% of the samples were behavioral in standard Monte-Carlo sampling without preselection. The GPE method also provided local sensitivities at minimal additional costs.

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“…The aim here is to mimic the behavior of the model while requiring less computational time to simulate the response. Hence, different meta-models have been developed in the community but Kriging and Polynomial Chaos Expansion (PCE) are considered to be the most used to treat UQ, see [30,31,32,33,34,35] among others. In the current work, accounting for the complex physics in acoustics, the PCE is preferred over the Kriging in order to ensure convergence as stated in [33].…”

Section: Introductionmentioning

confidence: 99%

Stochastic model reduction for polynomial chaos expansion of acoustic waves using proper orthogonal decomposition

Moçayd¹,

Mohamed

Ouazar³

et al. 2020

Reliability Engineering & System Safety

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Review and comparison of two meta-model-based uncertainty propagation analysis methods in groundwater applications: polynomial chaos expansion and Gaussian process emulation

Cited by 32 publications

References 85 publications

Incorporating spatial association into statistical classifiers: local pattern-based prior tuning

Incorporating spatial association into statistical classifiers: local pattern-based prior tuning

Sampling behavioral model parameters for ensemble-based sensitivity analysis using Gaussian process emulation and active subspaces

Stochastic model reduction for polynomial chaos expansion of acoustic waves using proper orthogonal decomposition

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