An integrated logit model for contamination event detection in water distribution systems

Mashor Housh, Avi Ostfeld

Research output: Contribution to journalArticlepeer-review


The problem of contamination event detection in water distribution systems has become one of the most challenging research topics in water distribution systems analysis. Current attempts for event detection utilize a variety of approaches including statistical, heuristics, machine learning, and optimization methods. Several existing event detection systems share a common feature in which alarms are obtained separately for each of the water quality indicators. Unifying those single alarms from different indicators is usually performed by means of simple heuristics. A salient feature of the current developed approach is using a statistically oriented model for discrete choice prediction which is estimated using the maximum likelihood method for integrating the single alarms. The discrete choice model is jointly calibrated with other components of the event detection system framework in a training data set using genetic algorithms. The fusing process of each indicator probabilities, which is left out of focus in many existing event detection system models, is confirmed to be a crucial part of the system which could be modelled by exploiting a discrete choice model for improving its performance. The developed methodology is tested on real water quality data, showing improved performances in decreasing the number of false positive alarms and in its ability to detect events with higher probabilities, compared to previous studies.

Original languageEnglish
Pages (from-to)210-223
Number of pages14
JournalWater Research
StatePublished - 5 May 2015

Bibliographical note

Publisher Copyright:
© 2015 Elsevier Ltd.


  • Event detection
  • Logit analysis
  • Water distribution systems
  • Water quality
  • Water security

ASJC Scopus subject areas

  • Water Science and Technology
  • Ecological Modeling
  • Pollution
  • Waste Management and Disposal
  • Environmental Engineering
  • Civil and Structural Engineering


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