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

  • Eyal BrillEmail author
Chapter
Part of the Protecting Critical Infrastructure book series (PCIN, volume 2)

Abstract

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.

Keywords

False Alarm Receiver Operating Characteristic Curve United States Environmental Protection Agency Unsupervised Learning Water Utility 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Technical Terms and Abbreviations

AWWA

American Water Works Associations

DT

Decision tree

EDS

Event detection system

LR

Logistic regression

ML

Machine learning

NN

Neural network

USEPA

Environmental Protection Agency

WQE

Water quality event

WQED

Water quality event detection

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Faculty of Technology Management (MOT)Holon Institute of TechnologyHolonIsrael

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