APPLICATION OF LINEAR SYSTEM'S THEORY AND LINEAR PROGRAMMING TO GROUND WATER MANAGEMENT IN KANSAS<sup>1</sup>

Heidari, Manoutchehr

doi:10.1111/j.1752-1688.1982.tb00108.x

Cited by 65 publications

(28 citation statements)

References 10 publications

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“…MODMAN (Greenwald 1998), SOMO1CS (SSOL 2009) module of SOMOS, and GWM-2005 (Ahlfeld et al 2009) are S-O models that use an RMM and MODFLOW as simulators. Many others used other simulators within groundwater flow S-O models (MorelSeytoux 1975;Gorelick 1982;Heidari 1982). CGU1, CGU2, and CGU4 are compatible RMMs (Peralta and Kalwij 2012).…”

Section: Response Matrix Methodsmentioning

confidence: 98%

Multi-model groundwater-management optimization: reconciling disparate conceptual models

Timani

Peralta

2015

Hydrogeol J

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Disagreement among policymakers often involves policy issues and differences between the decision makers' implicit utility functions. Significant disagreement can also exist concerning conceptual models of the physical system. Disagreement on the validity of a single simulation model delays discussion on policy issues and prevents the adoption of consensus management strategies. For such a contentious situation, the proposed multiconceptual model optimization (MCMO) can help stakeholders reach a compromise strategy. MCMO computes mathematically optimal strategies that simultaneously satisfy analogous constraints and bounds in multiple numerical models that differ in boundary conditions, hydrogeologic stratigraphy, and discretization. Shadow prices and trade-offs guide the process of refining the first MCMO-developed`multi-model strategy into a realistic compromise management strategy. By employing automated cycling, MCMO is practical for linear and nonlinear aquifer systems. In this reconnaissance study, MCMO application to the multilayer Cache Valley (Utah and Idaho, USA) river-aquifer system employs two simulation models with analogous background conditions but different vertical discretization and boundary conditions. The objective is to maximize additional safe pumping (beyond current pumping), subject to constraints on groundwater head and seepage from the aquifer to surface waters. MCMO application reveals that in order to protect the local ecosystem, increased groundwater pumping can satisfy only 40 % of projected water demand increase. To explore the possibility of increasing that pumping while protecting the ecosystem, MCMO clearly identifies localities requiring additional field data. MCMO is applicable to other areas and optimization problems than used here. Steps to prepare comparable sub-models for MCMO use are area-dependent.

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Section: Response Matrix Methodsmentioning

confidence: 98%

Multi-model groundwater-management optimization: reconciling disparate conceptual models

Timani

Peralta

2015

Hydrogeol J

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“…The area is bounded by two rivers on the western and southern direction as shown in Figure 1. The coverage area of the confined aquifer is approximately found to be 17.35 km 2 . The presence of two rivers on the western and southern sides of the aquifer defines the boundary condition to be a constant head, whereas no flow boundaries exist on the remaining north-east directions.…”

Section: Study Areamentioning

confidence: 95%

“…They used the least square method and linear programming for solving the optimization model. This approach was adopted by many researchers in the earlier stage of identification of unknown groundwater pollutant sources [2]- [7]. Although the evolvement gave various platforms for further research but one of the major disadvantages was the non-satisfactory performance while solving the non-linear system.…”

Section: Introductionmentioning

confidence: 99%

Identification of Unknown Groundwater Pollution Sources and Determination of Optimal Well Locations Using ANN-GA Based Simulation-Optimization Model

Leichombam¹,

Bhattacharjya²

2016

JWARP

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The linked simulation-optimization model can be used for solving a complex groundwater pollution source identification problem. Advanced simulators have been developed and successfully linked with numerous optimization algorithms for identification of groundwater pollution sources. However, the identification of pollution sources in a groundwater aquifer using linked simulation-optimization model has proven to be computationally expensive. To overcome this computational burden, an approximate simulator, the artificial neural network (ANN) model can be used as a surrogate model to replace the complex time-consuming numerical simulation model. However, for large-scale aquifer system, the performance of the ANN-based surrogate model is not satisfactory when a single ANN model is used to predict the concentration at different observation locations. In such a situation, the model efficiency can be enhanced by developing separate ANN model for each of the observation locations. The number of ANN models is equal to the number of observation wells in the aquifer. As a result, the complexity of the ANN-based simulation-optimization model will be related to the number of observation wells. Thus, this study used a modified formulation to find out the optimal numbers of observation wells which will eventually reduce the computational time of the model. The performance of the ANN-based simulation-optimization model is evaluated by identifying the groundwater pollutant sources of a hypothetical study area. The limited evaluation shows that the model has the potential for field application.

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“…During the 1970's the technique was expanded and brought into the hydrologic literature, principally by Wattenbarger (1970), Maddock (1972), Rosenwald andGreen (1974), andSchwarz (1976). Applications of the response matrix approach have been presented by Larson and others (1977) and by Heidari (1982) to determine "safe yield" of aquifers; by Willis (1983) to determine the optimal pumping scheme to meet agricultural water demands; by Danskin and Gorelick (1985) to evaluate the efficiency of a surface-water recharge program; and by Colarullo and others (1984), Atwood and Gorelick (1985), Gorelick and Wagner (1986), and Lefkoff and Gorelick (1985;1986) to contain plumes of contaminated ground water and design aquifer restoration systems.…”

Section: Ground-water Management Modeling Backgroundmentioning

confidence: 99%

AQMAN; linear and quadratic programming matrix generator using two-dimensional ground-water flow simulation for aquifer management modeling

Lefkoff¹,

Gorelick²

1987

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PREFACEThis report describes a computer model that combines ground-water flow simulation with mathematical optimization in order to develop and evaluate aquifer management strategies. The computer program is intended for general application and may have to be modified by the user for specific field problems. Although the program will produce reliable calculations for a wide variety of problems, the user is cautioned that in some cases the accuracy of management solutions can be significantly affected by user discretion during implementation.The user is requested to kindly notify the originating office of any errors found in this report or in the computer program. Updates may occasionally be made to both the report and the computer program. Users who wish to be added to the mailing list to receive updates, if any, may send a request to the following address: AQMAN U.S. Geological Survey

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APPLICATION OF LINEAR SYSTEM'S THEORY AND LINEAR PROGRAMMING TO GROUND WATER MANAGEMENT IN KANSAS¹

Cited by 65 publications

References 10 publications

Multi-model groundwater-management optimization: reconciling disparate conceptual models

Multi-model groundwater-management optimization: reconciling disparate conceptual models

Identification of Unknown Groundwater Pollution Sources and Determination of Optimal Well Locations Using ANN-GA Based Simulation-Optimization Model

AQMAN; linear and quadratic programming matrix generator using two-dimensional ground-water flow simulation for aquifer management modeling

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APPLICATION OF LINEAR SYSTEM'S THEORY AND LINEAR PROGRAMMING TO GROUND WATER MANAGEMENT IN KANSAS1

Cited by 65 publications

References 10 publications

Multi-model groundwater-management optimization: reconciling disparate conceptual models

Multi-model groundwater-management optimization: reconciling disparate conceptual models

Identification of Unknown Groundwater Pollution Sources and Determination of Optimal Well Locations Using ANN-GA Based Simulation-Optimization Model

AQMAN; linear and quadratic programming matrix generator using two-dimensional ground-water flow simulation for aquifer management modeling

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APPLICATION OF LINEAR SYSTEM'S THEORY AND LINEAR PROGRAMMING TO GROUND WATER MANAGEMENT IN KANSAS¹