Detecting Sanitary Sewer Overflows

Derya Yalcin, Kevin Lansey, Richard Sloan, Robert G. Decker, Jon C. Schladweiler

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

2 Scopus citations


Sanitary sewer overflows (SSO) are becoming of increasing concern to utilities and regulators as they pose a health risk. However, many overflows are not easily identifiable. A methodology is presented for detecting if a disruption in the system is occurring. The approach links neural networks as a prediction tool for expected flows and control limit theory for identifying significant deviations from the expected values that suggest an SSO occurrence. Detection depends upon the magnitude of the disruption, the relative distance between the disruption and the gage, the ability to analyze large quantities of monitoring data and the capacity of the developed methodology to compare real-time data with expected conditions. Data from Pima County Wastewater Management's monitoring system is used as a case study.

Original languageEnglish (US)
Title of host publicationWorld Water and Environmental Resources Congress
EditorsP. Bizier, P. DeBarry
Number of pages8
StatePublished - 2003
EventWorld Water and Environmental Resources Congress 2003 - Philadelphia, PA, United States
Duration: Jun 23 2003Jun 26 2003

Publication series

NameWorld Water and Environmental Resources Congress


OtherWorld Water and Environmental Resources Congress 2003
Country/TerritoryUnited States
CityPhiladelphia, PA

ASJC Scopus subject areas

  • Aquatic Science
  • Water Science and Technology


Dive into the research topics of 'Detecting Sanitary Sewer Overflows'. Together they form a unique fingerprint.

Cite this