Pre-posterior analysis as a tool for data evaluation: Application to aquifer contamination

Ben-Zvi, M.; Berkowitz, Brian; Kesler, S.

doi:10.1007/bf00421927

Cited by 28 publications

(22 citation statements)

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“…The stage where data values are unknown and only random potential data values can be used is called the preposterior stage [e.g., Ben-Zvi et al, 1988;James and Gorelick, 1994;Raiffa et al, 1995;TrainorGuitton et al, 2011].…”

Section: Preposterior Stage and Reliability Of A Designmentioning

confidence: 99%

“…[17] Goal-oriented approaches define the utility of sampling via ultimate measures, i.e., measures defined in the context of a given management application [e.g., Ben-Zvi et al, 1988;James and Gorelick, 1994;Feyen and Gorelick, 2005;Bhattacharjya et al, 2010;Li, 2010]. Thus, optimal sampling and field campaign strategies can adapt to the interplay between the actual information needs of the goal at hand, the available measurement and investigation techniques, and the specific composition of uncertainty [e.g., Maxwell et al, 1999;de Barros et al, 2009;Nowak et al, 2010].…”

Section: Introductionmentioning

confidence: 99%

“…The data worth concept follows classical ideas from utility theory and decision theory [e.g., Fishburn, 1970;Ben-Zvi et al, 1988;Raiffa et al, 1995]. It assigns monetary units for utility, and then weighs up expected benefits against the costs of field campaigns.…”

Section: Introductionmentioning

confidence: 99%

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A hypothesis‐driven approach to optimize field campaigns

Nowak¹,

Rubin²,

Barros³

2012

Water Resources Research

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[1] Most field campaigns aim at helping in specified scientific or practical tasks, such as modeling, prediction, optimization, or management. Often these tasks involve binary decisions or seek answers to yes/no questions under uncertainty, e.g., Is a model adequate? Will contamination exceed a critical level? In this context, the information needs of hydro(geo)logical modeling should be satisfied with efficient and rational field campaigns, e.g., because budgets are limited. We propose a new framework to optimize field campaigns that defines the quest for defensible decisions as the ultimate goal. The key steps are to formulate yes/no questions under uncertainty as Bayesian hypothesis tests, and then use the expected failure probability of hypothesis testing as objective function. Our formalism is unique in that it optimizes field campaigns for maximum confidence in decisions on model choice, binary engineering or management decisions, or questions concerning compliance with environmental performance metrics. It is goal oriented, recognizing that different models, questions, or metrics deserve different treatment. We use a formal Bayesian scheme called PreDIA, which is free of linearization, and can handle arbitrary data types, scientific tasks, and sources of uncertainty (e.g., conceptual, physical, (geo)statistical, measurement errors). This reduces the bias due to possibly subjective assumptions prior to data collection and improves the chances of successful field campaigns even under conditions of model uncertainty. We illustrate our approach on two instructive examples from stochastic hydrogeology with increasing complexity.

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Section: Preposterior Stage and Reliability Of A Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A hypothesis‐driven approach to optimize field campaigns

Nowak¹,

Rubin²,

Barros³

2012

Water Resources Research

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“…In this case, solving the OD problem will suggest i) which, among all possible observations, is the most valuable, and ii) whether to keep gathering information, or to stop and take action, accepting the present level of uncertainty. OD has been extensively applied in various domains, including water management (Yokota and Thompson 2004;Davis 1971;Maddock 1973;Bhattacharjya et al 2010;Reed and Kollat 2012), where applications focus mostly on remediation problems in groundwater systems (Ben-Zvi et al 1988;James and Gorelick 1994;Nowak et al 2012;Bierkens 2006;Kim and Lee 2007;Mantoglou and Kourakos 2007;Chadalavada and Datta 2008). In case the decision problem is a model selection problem, information-based cost functions are most appropriate (Nowak and Guthke 2016;Weijs et al 2010), while in economically important decisions, where actions are taken, a value of information approach can optimize benefits through the OD problem (Eidsvik and Ellefmo 2013;Trainor-Guitton et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Balancing Costs and Benefits in Selecting New Information: Efficient Monitoring Using Deterministic Hydro-economic Models

Raso

Weijs

Werner

2017

Water Resour Manage

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Uncertainty reduces reliability and performance of water system operations. A decision-maker can take action, accepting the present uncertainty and facing its risks, or reduce uncertainty by first obtaining additional information. Information, however, comes at a cost. The decision-maker must therefore efficiently use his/her resources, selecting the information that is most valuable. This can be done by solving the Optimal Design (OD) problem. The OD problem balances the cost of obtaining new information against its benefits, estimated as the added value of better informed actions. Despite its usefulness, the OD problem is not widely applied. Solving the OD problem requires a stochastic hydro-economic model, whereas most of hydro-economic models presently used are deterministic. In this paper we introduce an innovative methodology that makes deterministic hydro-economic models usable in the OD problem. In deterministic models, Least Squares Estimation (LSE) is often used to identify unknown parameters from data, making them, and hence the whole model, de facto stochastic. We advocate the explicit recognition of this uncertainty and propose to use it in risk-based decisions and formulation of the OD problem. The proposed methodology is illustrated on a flood warning scheme in the White Cart River, in Scotland, where a rating curve uncertainty can be reduced by extra gaugings. Gauging at higher flows is more informative but also more costly, and the LSE -OD formulation results in the economically optimal balance. We show the clear advantage of monitoring based on OD over monitoring uniquely based on uncertainty reduction.L. Raso

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“…4) In the context of epistemic model error for a complex synthetic nonlinear groundwater problem, the linear and nonlinear confidence and credible intervals for individual models performed similarly enough to indicate that the computationally frugal confidence intervals can be useful in many circumstances.…”

Section: )mentioning

confidence: 99%

Multi-Scale Assessment of Prediction Uncertainty in Coupled Reactive Transport Models Conducted at the Florida State University

Ye¹

2013

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IntroductionThis final project report summarizes research activities, findings, and deliverables obtained at the Florida State University (FSU) during the funding period of 2009 -2013. The goal of this project is: (1) to seek fundamental understanding of the factors causing and controlling predictive uncertainty in groundwater reactive transport modeling, and (2) to develop interdisciplinary tools for quantifying and reducing the predictive uncertainty. Uncertainty analysis in this project is focused on parametric uncertainty of individual models and model uncertainty between alternative models. The FSU project team was in charge of developing methods for uncertainty quantification and reduction. The methods have been applied to quantify predictive uncertainty in uranium reactive transport modeling at the Naturita Site.During this project period, we published twelve peer-reviewed journal articles (one is under revision and one under review) and four conference proceedings. In particular, the paper of published in Water Resources Research was selected as the Editor's Highlight for its new insights into faster computation of uncertainties. We presented our research results in multidisciplinary conferences such as geology, mathematics, geochemistry, and civil engineering. The publication on the SIAM News (Hill et al., 2012) introduced our interdisciplinary research results to members of the Society of Industrial and Applied Mathematics (SIAM). A master student (Geoffery Miller) and a doctoral student (Dan Lu) graduated with support of this project. The doctoral student received the 2012 Student Travel Fellowship and presented her research at the 2012 annual PI meeting; she is working as a post-doc at the Oak Ridge National Laboratory. We also collaborated with colleagues in the Los Alamos and Oak Ridge National Laboratories to help their research using the methods developed in this project. The collaboration produced one journal article (Dai et al., 2012) and one conference proceeding . Scientific Questions and Project FindingsThis project explored the scientific questions below related to various aspects of uncertainty analysis in coupled groundwater reactive transport models. Question 1: How does uncertainty behave for groundwater reactive transport models?This question was tackled by using the surface complexation model developed by Kohler et al. (1996). The model, denoted as C4 hereinafter, has two functional groups. The weak site, S 1 OH, is associated with one reaction: where S1 and S2 denote the adsorption sites. The uncertain parameters are the three equilibrium constants, logK1, logK2, and logK3 of the three reactions, respectively, and the fractions of the two sites. Since the fractions sum to one, the fraction of the strong site, logSite, is explicitly treated as an uncertain variable. Among the seven column experiments conducted by Kohler et al. (1996) to understand uranium reactive transport, three experiments, denoted as Expt_1, Expt_2, and Expt_8, were used in the analysis. They were performed unde...

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Pre-posterior analysis as a tool for data evaluation: Application to aquifer contamination

Cited by 28 publications

References 1 publication

A hypothesis‐driven approach to optimize field campaigns

A hypothesis‐driven approach to optimize field campaigns

Balancing Costs and Benefits in Selecting New Information: Efficient Monitoring Using Deterministic Hydro-economic Models

Multi-Scale Assessment of Prediction Uncertainty in Coupled Reactive Transport Models Conducted at the Florida State University

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