Value of Information as a Context-Specific Measure of Uncertainty in Groundwater Remediation

Liu, Xiaoyi; Lee, Jonghyun; Kitanidis, Peter K.; Parker, J. C.; Kim, Ungtae

doi:10.1007/s11269-011-9970-3

Cited by 22 publications

(17 citation statements)

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“…In general, it is difficult to compute VoI so that MC approach is proposed (Liu et al 2012; Pozzi and Kiureghian 2012; Straub 2013). VoI can be, however, computed easily in updating of Gaussian random field, i.e., Kriging, which is a probabilistic interpolation method (e.g.…”

Section: Quantification Of Voi In a Gaussian Random Fieldmentioning

confidence: 99%

Methodology of optimal sampling planning based on VoI for soil contamination investigation

Yoshida

2016

JGS Special Publication

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This paper proposes a method for optimal sampling planning, i.e., the number and placement of additional sampling for land with arbitrary shape and with existing sampling data, under the assumption of Gaussian random field with respect to a characteristic parameter. A set of optimal locations for additional sampling are evaluated as a solution of optimization problem, in which its objective function is Value of Information (VoI), and the optimization method is Particle Swarm Optimization. Optimal number of sampling is also evaluated by total cost, i.e., sum of observation cost and VoI. The balance of penalties and observation cost determines the optimal number of additional sampling.

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Section: Quantification Of Voi In a Gaussian Random Fieldmentioning

confidence: 99%

Methodology of optimal sampling planning based on VoI for soil contamination investigation

Yoshida

2016

JGS Special Publication

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“…For example, the classic study of James and Gorelick [1994] considered the selection of monitoring locations to minimize expected costs of both contaminant remediation and data collection. More recently, Liu et al [2012] presented a framework to determine the value of improved parameter information on expected contaminant remediation costs.…”

Section: Optimal Design Of Monitoring Networkmentioning

confidence: 99%

On the optimal design of experiments for conceptual and predictive discrimination of hydrologic system models

Kikuchi

Ferré

Vrugt

2015

Water Resources Research

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Experimental design and data collection constitute two main steps of the iterative research cycle (aka the scientific method). To help evaluate competing hypotheses, it is critical to ensure that the experimental design is appropriate and maximizes information retrieval from the system of interest. Scientific hypothesis testing is implemented by comparing plausible model structures (conceptual discrimination) and sets of predictions (predictive discrimination). This research presents a new Discrimination-Inference (DI) methodology to identify prospective data sets highly suitable for either conceptual or predictive discrimination. The DI methodology uses preposterior estimation techniques to evaluate the expected change in the conceptual or predictive probabilities, as measured by the Kullback-Leibler divergence. We present two case studies with increasing complexity to illustrate implementation of the DI for maximizing information withdrawal from a system of interest. The case studies show that highly informative data sets for conceptual discrimination are in general those for which between-model (conceptual) uncertainty is large relative to the within-model (parameter) uncertainty, and the redundancy between individual measurements in the set is minimized. The optimal data set differs if predictive, rather than conceptual, discrimination is the experimental design objective. Our results show that DI analyses highlight measurements that can be used to address critical uncertainties related to the prediction of interest. Finally, we find that the optimal data set for predictive discrimination is sensitive to the predictive grouping definition in ways that are not immediately apparent from inspection of the model structure and parameter values.

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“…Possible statistical measures for the data impact include, e.g., reduction of parameter or prediction variance, relative entropy [ Cover and Thomas , 2006, p. 19], or the possible significance level of hypothesis testing [ Nowak , 2008]. Although monetarization is not the primary interest of this study, our framework could easily account for monetary considerations similar to Liu et al . [ 2012].…”

Section: Approach and Contributionsmentioning

confidence: 99%

Bayesian assessment of the expected data impact on prediction confidence in optimal sampling design

Leube

Geiges

Nowak

2012

Water Resources Research

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[1] Incorporating hydro(geo)logical data, such as head and tracer data, into stochastic models of (subsurface) flow and transport helps to reduce prediction uncertainty. Because of financial limitations for investigation campaigns, information needs toward modeling or prediction goals should be satisfied efficiently and rationally. Optimal design techniques find the best one among a set of investigation strategies. They optimize the expected impact of data on prediction confidence or related objectives prior to data collection. We introduce a new optimal design method, called PreDIA(gnosis) (Preposterior Data Impact Assessor). PreDIA derives the relevant probability distributions and measures of data utility within a fully Bayesian, generalized, flexible, and accurate framework. It extends the bootstrap filter (BF) and related frameworks to optimal design by marginalizing utility measures over the yet unknown data values. PreDIA is a strictly formal information-processing scheme free of linearizations. It works with arbitrary simulation tools, provides full flexibility concerning measurement types (linear, nonlinear, direct, indirect), allows for any desired task-driven formulations, and can account for various sources of uncertainty (e.g., heterogeneity, geostatistical assumptions, boundary conditions, measurement values, model structure uncertainty, a large class of model errors) via Bayesian geostatistics and model averaging. Existing methods fail to simultaneously provide these crucial advantages, which our method buys at relatively higher-computational costs. We demonstrate the applicability and advantages of PreDIA over conventional linearized methods in a synthetic example of subsurface transport. In the example, we show that informative data is often invisible for linearized methods that confuse zero correlation with statistical independence. Hence, PreDIA will often lead to substantially better sampling designs. Finally, we extend our example to specifically highlight the consideration of conceptual model uncertainty.

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Value of Information as a Context-Specific Measure of Uncertainty in Groundwater Remediation

Cited by 22 publications

References 50 publications

Methodology of optimal sampling planning based on VoI for soil contamination investigation

Methodology of optimal sampling planning based on VoI for soil contamination investigation

On the optimal design of experiments for conceptual and predictive discrimination of hydrologic system models

Bayesian assessment of the expected data impact on prediction confidence in optimal sampling design

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