Detecting Sanitary Sewer Overflows

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

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

1 Citation (Scopus)

Abstract

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
Pages2321-2328
Number of pages8
StatePublished - 2003
EventWorld Water and Environmental Resources Congress 2003 - Philadelphia, PA, United States
Duration: Jun 23 2003Jun 26 2003

Other

OtherWorld Water and Environmental Resources Congress 2003
CountryUnited States
CityPhiladelphia, PA
Period6/23/036/26/03

Fingerprint

sewage systems
methodology
monitoring
gauges
monitoring system
health risk
neural networks
wastewater
gauge
case studies
prediction
detection
monitoring data

ASJC Scopus subject areas

  • Water Science and Technology
  • Aquatic Science

Cite this

Yalcin, D., Lansey, K. E., Sloan, R., Decker, R. G., & Schladweiler, J. C. (2003). Detecting Sanitary Sewer Overflows. In P. Bizier, & P. DeBarry (Eds.), World Water and Environmental Resources Congress (pp. 2321-2328)

Detecting Sanitary Sewer Overflows. / Yalcin, Derya; Lansey, Kevin E; Sloan, Richard; Decker, Robert G.; Schladweiler, Jon C.

World Water and Environmental Resources Congress. ed. / P. Bizier; P. DeBarry. 2003. p. 2321-2328.

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

Yalcin, D, Lansey, KE, Sloan, R, Decker, RG & Schladweiler, JC 2003, Detecting Sanitary Sewer Overflows. in P Bizier & P DeBarry (eds), World Water and Environmental Resources Congress. pp. 2321-2328, World Water and Environmental Resources Congress 2003, Philadelphia, PA, United States, 6/23/03.
Yalcin D, Lansey KE, Sloan R, Decker RG, Schladweiler JC. Detecting Sanitary Sewer Overflows. In Bizier P, DeBarry P, editors, World Water and Environmental Resources Congress. 2003. p. 2321-2328
Yalcin, Derya ; Lansey, Kevin E ; Sloan, Richard ; Decker, Robert G. ; Schladweiler, Jon C. / Detecting Sanitary Sewer Overflows. World Water and Environmental Resources Congress. editor / P. Bizier ; P. DeBarry. 2003. pp. 2321-2328
@inproceedings{551b9ae50cb04afcb82dbc0988667f47,
title = "Detecting Sanitary Sewer Overflows",
abstract = "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.",
author = "Derya Yalcin and Lansey, {Kevin E} and Richard Sloan and Decker, {Robert G.} and Schladweiler, {Jon C.}",
year = "2003",
language = "English (US)",
isbn = "0784406855",
pages = "2321--2328",
editor = "P. Bizier and P. DeBarry",
booktitle = "World Water and Environmental Resources Congress",

}

TY - GEN

T1 - Detecting Sanitary Sewer Overflows

AU - Yalcin, Derya

AU - Lansey, Kevin E

AU - Sloan, Richard

AU - Decker, Robert G.

AU - Schladweiler, Jon C.

PY - 2003

Y1 - 2003

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=1642516984&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=1642516984&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:1642516984

SN - 0784406855

SN - 9780784406854

SP - 2321

EP - 2328

BT - World Water and Environmental Resources Congress

A2 - Bizier, P.

A2 - DeBarry, P.

ER -