2015
DOI: 10.3390/systems3040287
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Exploring Tradeoffs in Demand-Side and Supply-Side Management of Urban Water Resources Using Agent-Based Modeling and Evolutionary Computation

Abstract: Abstract:Urban water supply systems may be managed through supply-side and demand-side strategies, which focus on water source expansion and demand reductions, respectively. Supply-side strategies bear infrastructure and energy costs, while demand-side strategies bear costs of implementation and inconvenience to consumers. To evaluate the performance of demand-side strategies, the participation and water use adaptations of consumers should be simulated. In this study, a Complex Adaptive Systems (CAS) framework… Show more

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Cited by 11 publications
(9 citation statements)
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References 34 publications
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“…Assessment tools [80], [97] City logistics [18] Decision support [16], [22], [24], [31], [35], [36], [40], [41], [50], [67], [68], [74], [ 77], [86], [100] Optimization [56], [57], [99] Planning [17], [21], [73], [88] Policy making [19], [28], [39], [46], [53], [64], [65], [72], [78], [90], [91], [92], [102] Prediction [27] System understanding [20], [23], [25], [26], [29], [30], [32], [33], [37], [38], [42]…”
Section: Managementmentioning
confidence: 99%
See 1 more Smart Citation
“…Assessment tools [80], [97] City logistics [18] Decision support [16], [22], [24], [31], [35], [36], [40], [41], [50], [67], [68], [74], [ 77], [86], [100] Optimization [56], [57], [99] Planning [17], [21], [73], [88] Policy making [19], [28], [39], [46], [53], [64], [65], [72], [78], [90], [91], [92], [102] Prediction [27] System understanding [20], [23], [25], [26], [29], [30], [32], [33], [37], [38], [42]…”
Section: Managementmentioning
confidence: 99%
“…NetLogo [26], [29], [32], [44], [47], [51], [55], [60], [63], [69], [71], [87], [101] Repast [20], [21] MASON [79], [80], [93], [102] AnyLogic [22], [56], [57], [94] MP-MAS [78], [83], [84] Other (MASS, CORMAS, Protégé, STELLA, MATLAB, SWARM, MacStarLogo, MatSim, GAMA, TAPAS) [18], [25], [27], [30], [33], [35], [41], [52], [60], [62], [74], [77], [90], [95] Custom [16], [28], [31], [36], [42], [45], [46], [49], [50], [58],…”
Section: Simulation Platform Referencesmentioning
confidence: 99%
“…Agent-based modeling has been applied to simulate a range of water resources planning problems by representing water users, stakeholders, and decision-makers as agents to capture decisions and behaviors around water use, water supply, wastewater services, and stormwater runoff [11]. Agent-based models have been applied to represent a population of residential water users that adapt their water consumption based on economics, climate, policies, and social influence [61][62][63][64][65][66][67][68][69][70][71]. These models simulate household decisions to use water and reduce consumption by adopting water-efficient technologies and restricting water use.…”
Section: Agent-based Modeling For Water Infrastructurementioning
confidence: 99%
“…These models simulate household decisions to use water and reduce consumption by adopting water-efficient technologies and restricting water use. Some frameworks couple agent-based modeling with the water supply system to capture feedback between the availability of water resources and decisions to conserve water [64][65][66][67]70]. Other agent-based models couple a population of agents with the hydraulic simulation of a water distribution system to evaluate how network flows are impacted by changing demands.…”
Section: Agent-based Modeling For Water Infrastructurementioning
confidence: 99%
“…For supply-side management, policies include public investment in water supply, including manufacturing of water from the ocean (desalination) and treating wastewater to potable conditions for human consumption, or treating wastewater for use in agriculture or for aquifer recharge [16,33]. Many studies have been conducted to assess the efficiency of such policy interventions in specific regions or countries [20,30].…”
Section: Introductionmentioning
confidence: 99%