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Implementing Machine Learning Algorithms for Water Quality Event Detection: Theory and Practice

Part of the Protecting Critical Infrastructure book series (PCIN,volume 2)


The current chapter examines the implementation of machine learning for water quality event detection. Two alternatives—supervised and unsupervised - are examined. It can be seen that the former performs better with the existence of historical classified true events, while the latter is preferable when history is not a factor. Water properties and their influence on methodology performance are also examined. The paper explains that TOC is preferred when false negative errors are rare or when experts' probability to make mistakes is low. The manuscript provides a numerical example that illustrates the two above-mentioned methodologies.


  • False Alarm
  • Receiver Operating Characteristic Curve
  • United States Environmental Protection Agency
  • Unsupervised Learning
  • Water Utility

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  • DOI: 10.1007/978-3-319-01092-2_4
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  1. 1.

    Results from this project have been presented at the annual American Water Works Association conference in 2011, but the official report has not yet been published.

  2. 2.



American Water Works Associations


Decision tree


Event detection system


Logistic regression


Machine learning


Neural network


Environmental Protection Agency


Water quality event


Water quality event detection


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Correspondence to Eyal Brill .

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Appendix A

Appendix A

Following is a list of websites for common EDS systems

Gardian Blue by Hach:

Canary by Sandia Labs:

Moni::tool by S::can:

BlueBox by WhiteWater:

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Brill, E. (2014). Implementing Machine Learning Algorithms for Water Quality Event Detection: Theory and Practice. In: Clark, R., Hakim, S. (eds) Securing Water and Wastewater Systems. Protecting Critical Infrastructure, vol 2. Springer, Cham.

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