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Activity Number:
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410
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 5, 2009 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #304707 |
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Title:
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Modeling Uncertainty for Storm Water Quantity and Quality Analysis Models in Urban DC
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Author(s):
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Valbona Bejleri*+ and Tolessa Deksissa
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Companies:
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University of the District of Columbia and University of the District of Columbia
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Address:
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4200 Connecticut Avenue, N.W., Washington, DC, 20008,
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Keywords:
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rainfall-runoff ; simulation model ; sensitivity analysis ; noninformative ; model averaging ; bootstrap
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Abstract:
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The dynamic rainfall-runoff simulation models used for single event or long-term (continuous) simulation of runoff quantity and quality from primarily urban areas are investigated, i.e. the EPA Storm Water Management Model (SWMM) and Integrated Urban Wastewater System (IUWS) model. We consider the development of a new model that will adjust for both model and parameter uncertainties. Bayesian model averaging technique with informative and non informative priors, sensitivity analysis and variable selection will be discussed. Monte-Carlo simulations and bootstrap sampling will be utilized to validate the model and adjust for uncertainty. A real case study will be presented as an application.
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