Simultaneous parameter estimation and contaminant source characterization for coupled groundwater flow and contaminant transport modelling

Wagner, Brian J.

doi:10.1016/0022-1694(92)90092-a

Cited by 202 publications

(100 citation statements)

References 16 publications

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“…The Wagner and Gorelick (1987) effort is particularly relevant because the results were used for quantifying the uncertainty in model predictions through the use of a first-order uncertainty analysis (Dettinger and Wilson 1981). A few of the more recent efforts to formulate and apply inverse methods for coupled flow and transport parameter and source term estimation, complete with an estimate of the uncertainty, include Wagner (1992), Sidauruk et al (1998), and Mayer and Huang (1998).…”

Section: Indirect Inverse Methodsmentioning

confidence: 99%

Uncertainty Analysis Framework - Hanford Site-Wide Groundwater Flow and Transport Model

Cole¹,

Bergeron²,

Murray³

et al. 2001

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SummaryAt the request of the U.S. Department of Energy (DOE), Pacific Northwest National Laboratory (PNNL) embarked on a new initiative to strengthen the technical defensibility of the Hanford site-wide groundwater model (SGM) used to make groundwater flow and transport predictions at the U.S. Department of Energy's Hanford Site in southeastern Washington State (Figure 1.1). The initial focus of the initiative is the characterization of major uncertainties in the current conceptual model that would affect model predictions. The long-term goals of the initiative are the development and implementation of a stochastic uncertainty estimation methodology in future assessments and analyses using the site-wide groundwater model. This report focuses on the development and implementation of the uncertainty analysis framework. The overall technical approach for this framework will closely follow the recommendations of the SGM external peer review panel made in 1999 (Gorelick et al. 1999). As suggested by the panel, the framework being developed acknowledges the inherent uncertainty in conceptual model representations and associated model inputs and thus in any predictions. This new framework acknowledges that prescribed processes, physical features, initial and boundary conditions, system stresses, field data, and model parameter values are not known and cannot be known with certainty and, as a result, predictions of heads and concentrations in three dimensions over time will be uncertain as well. The approach will specifically address those areas of special interest that were identified by the expert panel. These include uncertainty related to• alternative model structures and constructs of processes (e.g., different zonation, different boundary conditions, large-scale features, stresses, and chemical reactions)• model parameters• model scale and resolution issues.This report represents one of the first steps in development of this new SGM uncertainty framework by• identifying the types of assessments for which the SGM will likely be applied (Section 2)• discussing the various sources of uncertainty and the issues associated with the assessment of uncertainty as they relate to the development of this new framework for assessment of uncertainty in the Hanford SGM predictions. The sources of uncertainty include the most common high-level categories: uncertainty resulting from natural variability, model structure, and model parameters (Section 3).• providing a detailed description of the current conceptual model of the Hanford Site unconfined aquifer system that includes an assessment of the uncertainties and issues associated with that model (Section 4), the well picks used in the current interpretation of the hydrostratigraphic structure (Appendix A), and the results of a literature search that developed an initial bibliography and iii assessment regarding the various potential intercommunication mechanisms between the unconfined aquifer system and the uppermost confined aquifer system in the underlying basalts (Appendix ...

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Section: Indirect Inverse Methodsmentioning

confidence: 99%

Uncertainty Analysis Framework - Hanford Site-Wide Groundwater Flow and Transport Model

Cole¹,

Bergeron²,

Murray³

et al. 2001

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“…Some of the initial contributions in identification of unknown groundwater pollution sources proposed the use of linear optimization model based on linear response matrix approach (Gorelick et al, 1983) and statistical pattern recognition (Datta et al, 1989). Some of the important contributions to solve the unknown groundwater pollution sources identification problem include: non-linear maximum likelihood estimation based inverse models to determine optimal estimates of the unknown model parameters and source characteristics (Wagner, 1992); minimum relative entropy, a gradient based optimization for solving source identification problems (Woodbury et al, 1998); embedded nonlinear optimization technique for source identification (Mahar and Datta, 1997; inverse procedures based on correlation coefficient optimization (Sidauruk et al, 1997); Genetic Algorithm (GA) based approach (Aral et al, 2001;Singh & Datta, 2006); Artificial Neural Network (ANN) approach , 2007; constrained robust least square approach (Sun et al, 2006); classical optimization based approach (Datta et al, 2009a; inverse particle tracking approach (Bagtzoglou, 2003;Ababou et al, 2010); heuristic harmony search for source identification (Ayvaz, 2010); Simulated Annealing (SA) as optimization for source identification (Jha & Datta, 2011;Prakash & Datta, 2012, 2013, 2014a. A review of different optimization techniques for solving source identification problem is presented in Chadalavada et al (2011) and Amirabdollahian and Datta (2013).…”

Section: B Datta Et Al 42mentioning

confidence: 99%

Optimal Unknown Pollution Source Characterization in a Contaminated Groundwater Aquifer—Evaluation of a Developed Dedicated Software Tool

Datta¹,

Prakash²,

Cassou³

et al. 2014

GEP

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“…Then the approach optimizes a solution that is best-fitted with the corresponding measured data. This approach has been widely applied in identifying groundwater pollution source as linear optimization method [12], maximum likelihood method [13], and nonlinear optimization method [14]. As an example, Arvelo et al (2002) [6] have used a modified multi-zone model to study the optimal placement of chemical/biological warfare agent sensors in a building with nine offices and a hallway.…”

Section: The Optimization Approachmentioning

confidence: 99%

Fundamentals of Direct Inverse CFD Modeling to Detect Air Pollution Sources in Urban Areas

Bady¹

2013

CWEEE

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This paper presents the fundamentals of direct inverse modeling using CFD simulations to detect air pollution sources in urban areas. Generally, there are four techniques used for detecting pollution sources: the analytical technique, the optimization technique, the probabilistic technique, and the direct technique. The study discusses the potentialities and limits of each technique, where the direct inverse technique is focused. Two examples of applying the direct inverse technique in detecting pollution source are introduced. The difficulties of applying the direct inverse technique are investigated. The study reveals that the direct technique is a promising tool for detecting air pollution source in urban environments. However, more efforts are still needed to overcome the difficulties explained in the study.

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Simultaneous parameter estimation and contaminant source characterization for coupled groundwater flow and contaminant transport modelling

Cited by 202 publications

References 16 publications

Uncertainty Analysis Framework - Hanford Site-Wide Groundwater Flow and Transport Model

Uncertainty Analysis Framework - Hanford Site-Wide Groundwater Flow and Transport Model

Optimal Unknown Pollution Source Characterization in a Contaminated Groundwater Aquifer—Evaluation of a Developed Dedicated Software Tool

Fundamentals of Direct Inverse CFD Modeling to Detect Air Pollution Sources in Urban Areas

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