Water distribution systems event detection

Lina Perelman, Jonathan Arad, Nurit Oliker, Avi Ostfeld, Mashor Housh

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Since the events of 9/11 2001 in the US the world public awareness to possible terrorist attacks on water supply systems has increased dramatically, causing the security of drinking water distribution systems to become a major concern around the globe. Among the different threats, a deliberate chemical or biological contaminant injection is the most difficult to address, both as a consequence of the uncertainty surrounding the type of the injected contaminant and its consequences, as well as the uncertainty of location and time of the injection. In principle, a pollutant can be injected at any water distribution system connection (node) using a pump or a mobile pressurized tank. Although backflow preventers provide an obstacle to such actions, they do not exist at all connections, and at some might not be functional. This paper describes recent effort modeling of Avi Ostfeld's research team on water distribution systems event detection. The basic event detection framework is entitled AEDA (Aquatic Event Detection Algorithm) which utilizes Artificial Neural Networks (ANNs) for studying the interactions between multivariate water quality parameters and detecting possible outliers. Other layers on top of AEDA explore tradeoffs among contamination event parameters and improving its performance capabilities. Those and AEDA are reviewed in this paper.

Original languageEnglish
Title of host publication2nd IEEE Workshop on Complexity in Engineering, COMPENG 2012
Pages7-9
Number of pages3
DOIs
StatePublished - 2012
Externally publishedYes
Event2nd IEEE Workshop on Complexity in Engineering, COMPENG 2012 - Aachen, Germany
Duration: 11 Jun 201213 Jun 2012

Conference

Conference2nd IEEE Workshop on Complexity in Engineering, COMPENG 2012
Country/TerritoryGermany
CityAachen
Period11/06/1213/06/12

Keywords

  • Artificial Neural Networks
  • Bayesian analysis
  • Event detection
  • Water distribution systems

ASJC Scopus subject areas

  • Engineering (miscellaneous)

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