Multiobjective design of water distribution systems under uncertainty

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Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions (2014, background): “In addition, the selected uncertain variables are sometimes modelled in a very simplified way, without giving necessary consideration to spatial and temporal correlations, despite the fact that these can have important implications (Kapelan et al., 2005).”
Approximate methods for uncertainty analysis of water distribution systems (2009, unknown): “Kapelan et al. (2005, 2006) applied Latin hypercube sampling (LHS) to consider uncertainty in input parameters (nodal demand and pipe roughness) when optimising a WDS design.”
Stochastic sampling design using a multi-objective genetic algorithm and adaptive neural networks (2009, methodology): “Therefore, the selection of appropriate collection locations, called the sampling design (SD), is important and has been a challenge for researchers and practitioners (Kapelan et al., 2005a).”
Fuzzy probabilistic design of water distribution networks (2011, methodology): “It is widely recognized that the WDS design or rehabilitation process abounds in various uncertainty sources [ Kapelan et al., 2005 ]: (1) future water demands and pressure head requirements that are difficult to predict with any accuracy; (2) future system characteristics such as pipe roughness due”