Cost‐effective allocation of watershed management practices using a genetic algorithm

Arabi, Mazdak; Govindaraju, Rao S.; Hantush, Mohamed M.

doi:10.1029/2006wr004931

Cited by 185 publications

(119 citation statements)

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“…Government agencies promote implementation of NPS pollution control practices by cost-sharing with land owners and farmers that is deemed as a field-by-filed approach [WilliaVeith et al, 2003]. Recent studies have shown that holistic watershed-scale strategies for targeted implementation of conservation practices could enhance environmental benefits gained from conservation expenditure [Arabi et al, 2006;Rao et al, 2007;Maringanti et al, 2011].…”

Section: Introductionmentioning

confidence: 99%

“…Multiobjective optimization methods present an alternative approach that can effectively explore the tradeoffs between socioeconomic and environmental factors. While earlier studies utilized single-objective optimization approach for optimal allocation of conservation practices [WilliaVeith et al, 2003;Arabi et al, 2006], more recent efforts have focused on the development and application of multiobjective approaches [Maringanti et al, 2008;Jha et al, 2009].…”

Section: Introductionmentioning

confidence: 99%

“…Thus, evaluation of the performance of all possible combinations of control strategies for all fields within the watershed is infeasible. Recent studies have shown that optimization methods hold great promise for optimal allocation of NPS pollution control measures at the watershed scale [e.g., Veith, 2002;Arabi et al, 2006;Jha et al, 2009;Rabotyagov et al, 2010]. The watershed planning process is inherently a multiobjective problem.…”

Section: Introductionmentioning

confidence: 99%

“…However, many real world problems consist of continuous decision variables. While, most of the conservation practice optimization studies considered binary decision variables (1s and 0s, respectively, indicating that the corresponding conservation practice ''be'' or ''not be'' implemented) [Veith et al, 2004;Arabi et al, 2006], recent studies have dealt with discrete (integer) decision variables [Maringanti et al, 2011]. Incorporating continuous decision variables may lead to the selection of more realistic and better solutions in terms of the final ''optimal'' set of type and placement conservation practices, also known as ''Pareto-optimal front'' solutions.…”

Section: Introductionmentioning

confidence: 99%

“…[7] The genetic algorithm (GA) and its extensions have been used as an efficient and effective tool for optimal placement of conservation practices [Veith et al, 2004;Arabi et al, 2006;Maringanti et al, 2009] because, among other pragmatic considerations, it does not require differentiability and continuity for the objective and constraint functions. GA, originally proposed by Holland [1975], mimics the process of natural evolution such as selection, crossover, and mutation, referred to as genetic operators.…”

Section: Introductionmentioning

confidence: 99%

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A mixed discrete-continuous variable multiobjective genetic algorithm for targeted implementation of nonpoint source pollution control practices

et al. 2013

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[1] Implementation of nonpoint source (NPS) pollution control strategies at the watershed scale hinges on abating pollutant movement from the landscape to water bodies at minimum cost. This paper presents an integrated simulation-optimization approach for targeted implementation of agricultural conservation practices at the watershed scale. A multiobjective genetic algorithm (NSGA-II) with mixed discrete-continuous decision variables was coupled with a distributed watershed model, Soil and Water Assessment Tool (SWAT), to identify optimal types and locations of conservation practices for nutrient and pesticide control at the watershed scale. Previous optimization studies have used binary representation of nonpoint source pollution controls, even though many could be better characterized as continuous variables. In this study, a novel discrete-continuous decision variable, also known as mixed-variable, representation was used to enhance the versatility of the approach by evaluating more options during the search process. Application of the proposed framework in the Eagle Creek Watershed, Indiana, indicated that the optimal suite of conservation practices from the mixed-variable NSGA-II was more effective in meeting water quality targets at lower costs than the solution from binary-variable optimization. However, the mixed-variable approach was considerably more computationally demanding for assessing tradeoffs between environmental and economic factors. A method for hybridization of binary and mixed-variable NSGA-II methods in the context of nonpoint source pollution control practices was developed to enhance the computational efficiency of the optimization procedure. As a result, the number of model simulations required for convergence to the Pareto-optimal solutions was reduced by 96%. The conceptual complexity and computational requirements of optimization-based approaches are impediments to their wider application for targeted implementation of NPS pollution control strategies. The methods and finding of this study address these issues and could result in a more effective implementation of management strategies at the watershed scale. Hoag, and B. A. Engel (2013), A mixed discrete-continuous variable multiobjective genetic algorithm for targeted implementation of nonpoint source pollution control practices, Water Resour. Res., 49,[8344][8345][8346][8347][8348][8349][8350][8351][8352][8353][8354][8355][8356]

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A mixed discrete-continuous variable multiobjective genetic algorithm for targeted implementation of nonpoint source pollution control practices

et al. 2013

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Cost of water for peace and the environment inIsrael: An integrated approach

Ward

Becker

2015

Water Resources Research

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This paper presents a framework for discovering an economically viable water sharing plan among neighboring communities for promoting peace and environmental protection. Its application is to the Middle East in which Israel may be facing water supply obligations to address environmental requirements and for a possible a peace agreement with its Palestinian neighbors. The framework consists of integrating external factors, constraints, policy instruments, and targets. Our findings from a constrained optimization analysis of Israel's national water system show that the costs of increased deliveries are dependent on two major issues: (1) achieving integrated water resources management (IWRM) in which efficient combinations of expansion from several supply sources and reductions in demands occur over time, and (2) the cost of desalination technologies. We identify a $US 1.46 billion price tag, in present value terms, from using integrated management of demand reduction and supply expansion under current desalination costs. Adjustment costs will decline both with anticipated reductions in desalination costs and with an efficient implementation of IWRM. These adjustments can contribute to moderating regional tensions and protecting key ecological assets while addressing water scarcity in a volatile corner of the world.

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Interactive genetic algorithm for user‐centered design of distributed conservation practices in a watershed: An examination of user preferences in objective space and user behavior

Piemonti

Babbar-Sebens

Mukhopadhyay

et al. 2017

Water Resources Research

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Interactive Genetic Algorithms (IGA) are advanced human‐in‐the‐loop optimization methods that enable humans to give feedback, based on their subjective and unquantified preferences and knowledge, during the algorithm's search process. While these methods are gaining popularity in multiple fields, there is a critical lack of data and analyses on (a) the nature of interactions of different humans with interfaces of decision support systems (DSS) that employ IGA in water resources planning problems and on (b) the effect of human feedback on the algorithm's ability to search for design alternatives desirable to end‐users. In this paper, we present results and analyses of observational experiments in which different human participants (surrogates and stakeholders) interacted with an IGA‐based, watershed DSS called WRESTORE to identify plans of conservation practices in a watershed. The main goal of this paper is to evaluate how the IGA adapts its search process in the objective space to a user's feedback, and identify whether any similarities exist in the objective space of plans found by different participants. Some participants focused on the entire watershed, while others focused only on specific local subbasins. Additionally, two different hydrology models were used to identify any potential differences in interactive search outcomes that could arise from differences in the numerical values of benefits displayed to participants. Results indicate that stakeholders, in comparison to their surrogates, were more likely to use multiple features of the DSS interface to collect information before giving feedback, and dissimilarities existed among participants in the objective space of design alternatives.

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Cost‐effective allocation of watershed management practices using a genetic algorithm

Cited by 185 publications

References 16 publications

A mixed discrete-continuous variable multiobjective genetic algorithm for targeted implementation of nonpoint source pollution control practices

A mixed discrete-continuous variable multiobjective genetic algorithm for targeted implementation of nonpoint source pollution control practices

Cost of water for peace and the environment inIsrael: An integrated approach

Interactive genetic algorithm for user‐centered design of distributed conservation practices in a watershed: An examination of user preferences in objective space and user behavior

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