Review of Ground‐Water Quality Monitoring Network Design

Loáiciga, Hugo A.; Charbeneau, Randall J.; Everett, Lorne G.; Fogg, Graham E.; Hobbs, Benjamin F.; Rouhani, Shahrokh

doi:10.1061/(asce)0733-9429(1992)118:1(11)

Cited by 214 publications

(86 citation statements)

References 71 publications

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“…Their optimization contained multiple objectives and produced an optimal front of solutions. Loaiciga et al (1992) give a review of groundwater sampling design.…”

Section: What Degree Of Confidence Is Associated With the Results?mentioning

confidence: 99%

Optimal Measurement Site Locations for Inverse Transient Analysis in Pipe Networks

Vítkovský

Liggett

Simpson

et al. 2003

J. Water Resour. Plann. Manage.

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The quality of leak detection and quantification, and calibration for friction coefficients, in pipelines and networks by the inverse transient method are dependent on the quantity and location of data measurement sites. This paper presents an approach for determining the configuration of measurement sites that produces optimal results. Three performance indicators, two that are based on A-and D-optimality criteria and one that is based on the sensitivities of the heads with respect to the parameters, show which configurations are superior. These are illustrated by two case studies, the first of which is a small pipe network in which all configurations are considered directly (fully enumerable) and the second is a larger pipe network in which statistics are drawn from a sampling of configurations. For the large network, a genetic algorithm-with a new crossover operator-performs a search of

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“…Their optimization contained multiple objectives and produced an optimal front of solutions. Loaiciga et al (1992) give a review of groundwater sampling design.…”

Section: What Degree Of Confidence Is Associated With the Results?mentioning

confidence: 99%

Optimal Measurement Site Locations for Inverse Transient Analysis in Pipe Networks

Vítkovský

Liggett

Simpson

et al. 2003

J. Water Resour. Plann. Manage.

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“…It optimizes the utility of sampling [e.g., Pukelsheim, 2006], which is traditionally defined as increased information [Bernardo, 1979] or reduced uncertainty (measured by variances, covariances, or entropies [e.g., Pukelsheim, 2006;Nowak, 2010;Abellan and Noetinger, 2010]). Unfortunately, these are only surrogate (approximate) measures for the actual utility, rather than ultimate measures [e.g., Loaiciga et al, 1992]. The use of surrogates may corrupt the optimality of designs for the originally intended purpose.…”

Section: Introductionmentioning

confidence: 99%

“…A second reason to use MOO is that there may be a multitude of competing or (seemingly) incompatible objectives by different stakeholders. Such objectives may as well evolve and change over time, especially in the design of long-term groundwater monitoring networks [e.g., Loaiciga et al, 1992;Reed and Kollat, 2012].…”

Section: Introductionmentioning

confidence: 99%

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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“…Several statistical methods can be applied to approach the problem. According to [5], these methods can be classified as simulations, variance-based techniques, and probability. Essentially, the difference among them lies in the formulation of the objective function to be optimized.…”

Section: Introductionmentioning

confidence: 99%

Optimization of a Groundwater Monitoring Network for a Sustainable Development of the Maheshwaram Catchment, India

Nabi

Gallardo²,

Ahmed

2011

Sustainability

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Abstract:Groundwater is one of the most valuable resources for drinking water and irrigation in the Maheshwaram Catchment, Central India, where most of the local population depends on it for agricultural activities. An increasing demand for irrigation and the growing concern about potential water contamination makes imperative the implementation of a systematic groundwater-quality monitoring program in the region. Nonetheless, limited funding and resources emphasize the need to achieve a representative but cost-effective sampling strategy. In this context, field observations were combined with a geostatistical analysis to define an optimized monitoring network able to provide sufficient and non-redundant information on key hydrochemical parameters. A factor analysis was used to evaluate the interrelationship among variables, and permitted to reduce the original dataset into a new configuration of monitoring points still able to capture the spatial variability in the groundwater quality of the basin. The approach is useful to maximize data collection and contributes to better manage the allocation of resources under budget constrains.

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Review of Ground‐Water Quality Monitoring Network Design

Cited by 214 publications

References 71 publications

Optimal Measurement Site Locations for Inverse Transient Analysis in Pipe Networks

Optimal Measurement Site Locations for Inverse Transient Analysis in Pipe Networks

A hypothesis‐driven approach to optimize field campaigns

Optimization of a Groundwater Monitoring Network for a Sustainable Development of the Maheshwaram Catchment, India

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