Aggregated surrogate simulator for groundwater-surface water management via simulation-optimization modeling: Theory, development and tests

Timani, Bassel; Peralta, Richard C.

doi:10.1016/j.envsoft.2017.06.014

Cited by 11 publications

(6 citation statements)

References 59 publications

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“…These computational challenges can be addressed using a number of strategies. A commonly used approach is replace computationally expensive simulation models with more efficient surrogate-, meta-or emulation-models (Razavi et al, 2012), which can increase the computational efficiency of EAs by one to two orders of magnitude (Beh et al, 2017;Broad et al, 2015b;Timani and Peralta, 2017). As EAs are easily parallelizable (see Section 4), the use of parallel computing can also be an effective means of reducing overall runtime (Newland et al, 2018;Tang et al, 2007).…”

Section: What Are the Challenges Associated With Using Eas?mentioning

confidence: 99%

Introductory overview: Optimization using evolutionary algorithms and other metaheuristics

Maier

Razavi

Kapelan

et al. 2019

Environmental Modelling & Software

212

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Environmental models are used extensively to evaluate the effectiveness of a range of design, planning, operational, management and policy options. However, the number of options that can be evaluated manually is generally limited, making it difficult to identify the most suitable options to consider in decision-making processes. By linking environmental models with evolutionary and other metaheuristic optimization algorithms, the decision options that make best use of scarce resources, achieve the best environmental outcomes for a given budget or provide the best tradeoffs between competing objectives can be identified. This Introductory Overview presents reasons for embedding formal optimization approaches in environmental decision-making processes, details how environmental problems are formulated as optimization problems and outlines how single-and multi-objective optimization approaches find good solutions to environmental problems. Practical guidance and potential challenges are also provided.

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Section: What Are the Challenges Associated With Using Eas?mentioning

confidence: 99%

Introductory overview: Optimization using evolutionary algorithms and other metaheuristics

Maier

Razavi

Kapelan

et al. 2019

Environmental Modelling & Software

212

View full text Add to dashboard Cite

show abstract

“…The most studied issue was using MBSO for control processes to optimize production parameters for certain goods. Most works (74.1%), addressed this problem type relative to managing water resources to find well-water injection and extraction pumping rates for supplying demand while reducing energy costs, wear and tear, and watershed contamination e.g., (Ataie-Ashtiani et al 2014;Candelieri et al, 2018;Hussain et al, 2015;Roy and Datta 2019a;Timani and Peralta 2017). Other applications were determining optimal parameters for plastic injection processes (Dang 2014;Villarreal-Marroquín et al, 2011, cutting parameters for semiconductor wires (Monostori and Viharos 2001), and sheet metal pressing (WANG et al, 2018).…”

Section: Nature Of Researchmentioning

confidence: 99%

Metamodel-Based Simulation Optimization: A Systematic Literature Review

Amaral¹,

Montevechi²,

Miranda³

2022

Uncertainty Modeling: Fundamental Concepts and Models

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Over the past few decades, metamodeling tools have received attention for their ability to represent and improve complex systems. The use of metamodeling techniques in optimization problems via simulation has grown considerably in recent years to promote more robust and agile decision-making, determining the best scenario among the solution space to the problem. In this sense, this Chapter discusses the state of the art of metamodeling-based simulation optimization, presenting the gaps, opportunities, and future perspectives found in literature.

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“…The knowledge that mathematical optimization yields better solutions to well-formulated optimization problems than does a trial-and-error approach, is so widespread that S-O modelling has been extensively employed for designing optimal water systems and strategies [14]. Examples are contamination remediation [14], conjunctive use of surface water and groundwater [14][15][16][17], irrigation management [18,19], crop planning [20,21], seawater intrusion management [22,23], groundwater-pumping optimization [24][25][26], drought analysis [27], climate impacts assessment [28], management of total suspended solids [29], and aquifer recharge management [30].…”

Section: Introductionmentioning

confidence: 99%

“…Refs. [14,26,34] describe the procedure for adapting a response matrix approach to accurately simulate nonlinear unconfined groundwater flow.…”

Section: Introductionmentioning

confidence: 99%

Simulation-Optimization for Conjunctive Water Resources Management and Optimal Crop Planning in Kushabhadra-Bhargavi River Delta of Eastern India

Jha

Peralta

Sahoo

2020

IJERPH

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Water resources sustainability is a worldwide concern because of climate variability, growing population, and excessive groundwater exploitation in order to meet freshwater demand. Addressing these conflicting challenges sometimes can be aided by using both simulation and mathematical optimization tools. This study combines a groundwater-flow simulation model and two optimization models to develop optimal reconnaissance-level water management strategies. For a given set of hydrologic and management constraints, both of the optimization models are applied to part of the Mahanadi River basin groundwater system, which is an important source of water supply in Odisha State, India. The first optimization model employs a calibrated groundwater simulation model (MODFLOW-2005, the U.S. Geological Survey modular ground-water model) within the Simulation-Optimization MOdeling System (SOMOS) module number 1 (SOMO1) to estimate maximum permissible groundwater extraction, subject to suitable constraints that protect the aquifer from seawater intrusion. The second optimization model uses linear programming optimization to: (a) optimize conjunctive allocation of surface water and groundwater and (b) to determine a cropping pattern that maximizes net annual returns from crop yields, without causing seawater intrusion. Together, the optimization models consider the weather seasons, and the suitability and variability of existing cultivable land, crops, and the hydrogeologic system better than the models that do not employ the distributed maximum groundwater pumping rates that will not induce seawater intrusion. The optimization outcomes suggest that minimizing agricultural rice cultivation (especially during the non-monsoon season) and increasing crop diversification would improve farmers’ livelihoods and aid sustainable use of water resources.

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Aggregated surrogate simulator for groundwater-surface water management via simulation-optimization modeling: Theory, development and tests

Cited by 11 publications

References 59 publications

Introductory overview: Optimization using evolutionary algorithms and other metaheuristics

Introductory overview: Optimization using evolutionary algorithms and other metaheuristics

Metamodel-Based Simulation Optimization: A Systematic Literature Review

Simulation-Optimization for Conjunctive Water Resources Management and Optimal Crop Planning in Kushabhadra-Bhargavi River Delta of Eastern India

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