State of the Art for Genetic Algorithms and Beyond in Water Resources Planning and Management

Nicklow, John W.; Reed, Patrick M.; Savić, Dragan; Dessalegne, Tibebe; Harrell, Laura J.; Hilton, Amy B. Chan; Karamouz, ‪Mohammad; Minsker, Barbara; Ostfeld, Avi; Singh, Abhishek; Zechman, Emily M.

doi:10.1061/(asce)wr.1943-5452.0000053

Cited by 527 publications

(281 citation statements)

References 228 publications

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“…The algorithms themselves are readily adaptable to a wide variety of application contexts (Back et al, 1997;Goldberg, 1989;Nicklow et al, 2010).…”

Section: Figurementioning

confidence: 99%

“…Coello et al, 2007;Deb, 2001) and EA use in water resources (e.g. Nicklow et al, 2010), and performs diagnostic assessments on water-related problems (e.g. .…”

Section: Purpose and Organisation Of This Position Papermentioning

confidence: 99%

“…model calibration, the planning, design and operation of water resources systems etc.) in many different application areas over the last few decades (Nicklow et al, 2010). Since metaheuristics were first applied in the water resources field (Dougherty and Marryott, 1991;McKinney and Lin, 1994;Ritzel et al, 1994), their popularity has increased dramatically, probably facilitated by the simultaneous increase of available computational power (Washington et al, 2009), to the point where they are widely used (Nicklow et al, 2010), even by actual water planning utilities (Basdekas, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…reproduction, mutation, crossover, selection, etc.) (Nicklow et al, 2010). Consequently, the focus of the remainder of this paper is on EAs, although many of the concepts discussed also broadly apply to other metaheuristics.…”

Section: Introductionmentioning

confidence: 99%

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Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

Maier

Kapelan

Kasprzyk

et al. 2014

Environmental Modelling & Software

521

317

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In all cases accepted manuscripts should:  link to the formal publication via its DOI  bear a CC-BY-NC-ND license -this is easy to do, click here to find out how  if aggregated with other manuscripts, for example in a repository or other site, be shared in alignment with our hosting policy  not be added to or enhanced in any way to appear more like, or to substitute for, the published journal article AbstractThe development and application of evolutionary algorithms (EAs) and other metaheuristics for the optimisation of water resources systems has been an active research field for over two decades. Research to date has emphasized algorithmic improvements and individual applications in specific areas (e.g., model calibration, water distribution systems, groundwater management, river-basin planning and management, etc.). However, there has been limited synthesis between shared problem traits, common EA challenges, and needed advances across major applications. This paper clarifies the current status and future research directions for better solving key water resources problems using EAs. Advances in understanding fitness landscape properties and their effects on algorithm performance are critical. Future EA-based applications to real-world problems require a fundamental shift of focus towards improving problem formulations, understanding general theoretic frameworks for problem decompositions, major advances in EA computational efficiency, and most importantly aiding real decision-making in complex, uncertain application contexts.

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“…The algorithms themselves are readily adaptable to a wide variety of application contexts (Back et al, 1997;Goldberg, 1989;Nicklow et al, 2010).…”

Section: Figurementioning

confidence: 99%

“…Coello et al, 2007;Deb, 2001) and EA use in water resources (e.g. Nicklow et al, 2010), and performs diagnostic assessments on water-related problems (e.g. .…”

Section: Purpose and Organisation Of This Position Papermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

Maier

Kapelan

Kasprzyk

et al. 2014

Environmental Modelling & Software

521

317

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“…Simulation and optimisation tools have been used extensively in water planning and management [1][2][3]. These computer modelling tools are used in various processes to support rational decision making and have their roots in systems analysis, operations research and decision sciences [4,5].…”

Section: Introductionmentioning

confidence: 99%

Serious Gaming for Water Systems Planning and Management

Savić

Morley

Khoury

2016

Water

Self Cite

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Water systems planning and management share the same roots with gaming, as they rely on concepts in systems analysis, operations research and decision sciences. This paper focuses on Serious Games (those used for purposes other than mere entertainment), with applications in the area of water systems planning and management. A survey of published work on gaming is carried out with particular attention given to applications of Serious Gaming to water systems planning and management. The survey is also used to identify the principal criteria for the classification of Serious Gaming for water related applications, including application areas, goals, number and type of players, user interface, type of simulation model used, realism of the game, performance feedback, progress monitoring and game portability. The review shows that game applications in the water sector can be a valuable tool for making various stakeholders aware of the socio-techno-economic issues related to managing complex water systems. However, the critical review also indicates a gap that exists in the Serious Game application area with the lack of water distribution system games. A conceptually simple, but computationally elaborate new game for water distribution system analysis, design and evaluation (SeGWADE) is presented in this paper. It has a main goal of finding a least-cost design for a well-known benchmark problem, for which the game environment takes the computational and visualisation burden away from the simulation tool and the player. The game has been evaluated in a classroom environment in which a high degree of player engagement with the game was observed, due to its basic game ingredients and activities, i.e., challenge, play and fun. In addition, a clear improvement in learning has been observed in how players attempted to identify solutions that satisfy the pressure criterion with players readily identifying the proximity of the better solutions to the starting, infeasible configuration. Through applications of Serious Gaming such as this, decision makers can learn about the complexity of the water distribution system design problem, experiment safely using a computer model of a real system, understand conflicting objectives (i.e., minimization of cost and satisfaction of minimum pressure) and develop strategies for coping with complexity without being burdened by the limitations of the ICT technology at their disposal.

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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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State of the Art for Genetic Algorithms and Beyond in Water Resources Planning and Management

Cited by 527 publications

References 228 publications

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

Serious Gaming for Water Systems Planning and Management

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

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