Predicting CSOs for real time decision support

D. J. Hill, Barbara S Minsker, A. Schmidt

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationProceedings of World Environmental and Water Resources Congress 2009 - World Environmental and Water Resources Congress 2009
Subtitle of host publicationGreat Rivers
Pages2210-2219
Number of pages10
DOIs
StatePublished - 2009
EventWorld Environmental and Water Resources Congress 2009: Great Rivers - Kansas City, MO, United States
Duration: May 17 2009May 21 2009

Publication series

NameProceedings of World Environmental and Water Resources Congress 2009 - World Environmental and Water Resources Congress 2009: Great Rivers
Volume342

Other

OtherWorld Environmental and Water Resources Congress 2009: Great Rivers
Country/TerritoryUnited States
CityKansas City, MO
Period5/17/095/21/09

ASJC Scopus subject areas

  • Environmental Science(all)

Fingerprint

Dive into the research topics of 'Predicting CSOs for real time decision support'. Together they form a unique fingerprint.

Cite this