Cost‐effective long‐term groundwater monitoring design using a genetic algorithm and global mass interpolation

Reed, Patrick M.; Minsker, Barbara; Valocchi, Albert J.

doi:10.1029/2000wr900232

Cited by 165 publications

(112 citation statements)

References 37 publications

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“…[8] GAs have been used in the past with various optimality criterion to develop optimal observation networks [Reed et al, 2000;McPhee and Yeh, 2006;Babbar-Sebens and Minsker, 2010]. However, many of these studies were challenged by the fact that GAs do not address the computational burden of the original model.…”

Section: Experimental Designmentioning

confidence: 99%

Experimental design for estimating unknown groundwater pumping using genetic algorithm and reduced order model

Ushijima

Yeh

2013

Water Resour. Res.

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[1] An optimal experimental design algorithm is developed to select locations for a network of observation wells that provide maximum information about unknown groundwater pumping in a confined, anisotropic aquifer. The design uses a maximal information criterion that chooses, among competing designs, the design that maximizes the sum of squared sensitivities while conforming to specified design constraints. The formulated optimization problem is non-convex and contains integer variables necessitating a combinatorial search. Given a realistic large-scale model, the size of the combinatorial search required can make the problem difficult, if not impossible, to solve using traditional mathematical programming techniques. Genetic algorithms (GAs) can be used to perform the global search ; however, because a GA requires a large number of calls to a groundwater model, the formulated optimization problem still may be infeasible to solve. As a result, proper orthogonal decomposition (POD) is applied to the groundwater model to reduce its dimensionality. Then, the information matrix in the full model space can be searched without solving the full model. Results from a small-scale test case show identical optimal solutions among the GA, integer programming, and exhaustive search methods. This demonstrates the GA's ability to determine the optimal solution. In addition, the results show that a GA with POD model reduction is several orders of magnitude faster in finding the optimal solution than a GA using the full model. The proposed experimental design algorithm is applied to a realistic, two-dimensional, large-scale groundwater problem. The GA converged to a solution for this large-scale problem.Citation: Ushijima, T. T., and W. W.-G. Yeh (2013), Experimental design for estimating unknown groundwater pumping using genetic algorithm and reduced order model, Water Resour. Res., 49,[6688][6689][6690][6691][6692][6693][6694][6695][6696][6697][6698][6699]

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Section: Experimental Designmentioning

confidence: 99%

Experimental design for estimating unknown groundwater pumping using genetic algorithm and reduced order model

Ushijima

Yeh

2013

Water Resour. Res.

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“…[137] Typical options are simplistic but efficient choices such as greedy search or sequential exchange [e.g., Christakos, 1992], classical stochastic search algorithms such as genetic algorithms [e.g., Reed et al, 2000aReed et al, , 2000b, or simulated annealing [e.g., Laarhoven and Aarts, 1992], or more modern versions such as the CMA-ES evolutionary search [Hansen et al, 2003]. A promising recent alternative is to combine different global and local search strategies, such as in the AMAL-GAM general-purpose optimization algorithm by Vrugt et al [2009].…”

Section: A3 Optimization Algorithmsmentioning

confidence: 99%

“…It can be used to optimize (1) what types of data (e.g., material parameters, state variables) to collect, (2) where to sample (e.g., the spatial layout and time schedule of observation networks), and (3) how to best excite the system to observe an informative response (e.g., designing tracer injections or hydraulic tests). Many applications in groundwater hydrology can be found in the literature [e.g., James and Gorelick, 1994;Reed et al, 2000a;Herrera and Pinder, 2005;Nowak et al, 2010;Leube et al, 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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“…The hillslope experiment illustrated in Figure 4d follows the same approach, with soil moisture/pressure sensors within the vadose zone, and piezometers below the water table, all deployed on a regular or irregular grid as desired for the purpose of closing water balances at the hillslope scale. Note that in each example, optimal locations for sensors can be identified by combining a priori field information with inverse models or stochastic uncertainty forecasts to meet experimental objectives (for reviews on network design, see Rodriguez-Iturbe and Mejia [1974], Langbein [1979], Sun [1994], Herrera de Olivares [1998], and Reed et al [2000]). …”

Section: Upscaling the Experiment: The Watershedmentioning

confidence: 99%

Bridging river basin scales and processes to assess human‐climate impacts and the terrestrial hydrologic system

Reed

Brooks

Davis

et al. 2006

Water Resources Research

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The increasing expression of human activity, climate variability, and climate change on humid, terrestrial hydrologic systems has made the integrated nature of large river basins more apparent. However, to date, there is no instrument platform sufficient to characterize river basins' hydrologic couplings and feedbacks, with many processes and impacts left almost entirely unobserved (e.g., snowmelt floods). Characterization at the river basin scale will require a more holistic vision and a far greater commitment from the environmental science community. It will require new designs and implementation of integrated instrumentation, a new generation of models, and a management framework that clearly addresses the human‐climate‐terrestrial interactions impacting our watersheds and river basins. Initially, we propose that existing “similarity classifications” (e.g., regional soil, geologic, ecologic, hydrographic digital products) can provide a starting point for organizing historical data and initiating a long‐term adaptive, multiscale observing strategy. This vision paper outlines instrumentation platforms for point, plot, reach, and hillslope scales that could be located within the “characteristic” landscapes of river basins. The network of observing platforms then forms the basis of a “Hydro‐Mesonet” that can potentially support multiscale, multiprocess scientific studies necessary to understand and improve forecasts of our water resources at the river basin scale. This paper concludes with a discussion of how a network of such sites can support research at the level of the individual researcher and scale to the level of community‐wide initiatives.

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Cost‐effective long‐term groundwater monitoring design using a genetic algorithm and global mass interpolation

Cited by 165 publications

References 37 publications

Experimental design for estimating unknown groundwater pumping using genetic algorithm and reduced order model

Experimental design for estimating unknown groundwater pumping using genetic algorithm and reduced order model

A hypothesis‐driven approach to optimize field campaigns

Bridging river basin scales and processes to assess human‐climate impacts and the terrestrial hydrologic system

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