Journal of Water Resources Planning and Management · 2018 · 22 citations · 18 references
EngineeringEnvironmental EngineeringWater Resources ManagementUncertainty QuantificationWater Quality EngineeringWater Resources EngineeringHydraulic ModelingUncertainty AnalysisWater Distribution SystemsWater QualityWater Supply EngineeringWater Supply SystemsContaminant Plume ModelingContaminant Transport ModelingDistribution System Water QualityDrinking Water ContaminantsSamplingSampling StrategiesWater Distribution ModelingWater Quality Sampling
Sampling of drinking water distribution systems is performed to ensure good water quality and protect public health. Sampling also satisfies regulatory requirements and is done to respond to customer complaints or emergency situations. Water distribution system modeling techniques can be used to plan and inform sampling strategies. However, a high degree of accuracy and confidence in the hydraulic and water quality models is required to support real-time response. One source of error in these models is related to uncertainty in model input parameters. Effective characterization of these uncertainties and their effect on contaminant transport during a contamination incident is critical for providing confidence estimates in model-based design and evaluation of different sampling strategies. In this paper, the effects of uncertainty in customer demand, isolation valve status, bulk reaction rate coefficient, contaminant injection location, start time, duration and rate on the size and location of the contaminant plume are quantified for two example water distribution systems. Results show that the most important parameter was the injection location. The size of the plume was also affected by the reaction rate coefficient, injection rate and the injection duration, while the the exact location of the plume was additionally affected by the isolation valve status. Uncertainty quantification provides a more complete picture of how contaminants move within a water distribution system and provides more information when using modeling results to select sampling locations.
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THE DESIGN OF OPTIMUM MULTIFACTORIAL EXPERIMENTS
R. L. Plackett, J. P. Burman · Biometrika · 1946 · 3.8K citations
Mathematical Statistics and Data Analysis
Eric R. Ziegel, John Rice · Technometrics · 1995 · 844 citations · Full text