Chapter
Apr 26, 2012
Predicting CSOs for Real Time Decision Support
Authors: D. J. Hill [email protected], B. Minsker [email protected], and A. Schmidt [email protected]Author Affiliations
Publication: World Environmental and Water Resources Congress 2009: Great Rivers
Abstract
This paper presents a novel data-driven method for modeling combined sewer overflows (CSOs) in real-time. This method treats CSO event generation as a threshold process that is triggered by increasingly intense rainfall events, and predicts the likelihood of a CSO given input conditions using a Bayesian network. The fusion of relevant data from multiple agencies into a unified data stream in real time is described, and a hierarchical modeling strategy is proposed that will facilitate the exploration of the causes of CSOs and direct research into the adaptive management of combined sewer systems using the Chicago wastewater system as a case study.
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© 2009 American Society of Civil Engineers.
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Published online: Apr 26, 2012
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ASCE Technical Topics:
- Analysis (by type)
- Bayesian analysis
- Business management
- Case studies
- Climates
- Combined sewers
- Decision making
- Decision support systems
- Engineering fundamentals
- Environmental engineering
- Flow (fluid dynamics)
- Fluid dynamics
- Fluid mechanics
- Hydrologic data
- Hydrologic engineering
- Hydrology
- Information management
- Infrastructure
- Lifeline systems
- Meteorology
- Methodology (by type)
- Overflow
- Practice and Profession
- Precipitation
- Rainfall
- Rainfall intensity
- Research methods (by type)
- Sewers
- Statistical analysis (by type)
- Water and water resources
Authors
Affiliations
National Center for Supercomputing Applications, University of Illinois, Urbana, IL, USA 61820. E-mail: [email protected]
Department of Civil and Environmental Engineering, University of Illinois, Urbana, IL, USA 61820. E-mail: [email protected]
Department of Civil and Environmental Engineering, University of Illinois, Urbana, IL, USA 61820. E-mail: [email protected]
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