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.
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Notes
- 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.
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
References
Berman O, Gavious A (2007) Location of terror response facilities: a game between state and terrorist. Eur J Oper Res 177(2007):1113–1133
Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. Chapman & Hall/CRC, New York
Chang N-B, Pongsanone NP, Ernest A (2012) A rule-based decision support system for sensor deployment in small drinking water networks. J Cleaner Prod 29–30:28–37
Cohen J (1960) A coefficient of agreement for nominal scales. Educ Psychol Measur 20(1):37–46
EPA (2005) WaterSentinel online water quality monitoring as an indicator of drinking water contamination. EPA 817-D-05-002
Helbling DE, VanBriesen JM (2008) Continuous monitoring of residual chlorine concentrations in response to controlled microbial intrusions in a laboratory-scale distribution system. Water Res 42(12):3162–3172
Mann J, Runge J (2010) State of the industry report 2010: how water professionals are meeting ongoing challenges and economic uncertainty. J AWWA 102:10
Quinlan JR (1986) Induction of decision trees. Mach Learn 1:81–106
Quinlan JR (1993) C4.5: programs for machine learning. Morgan Kaufmann Publishers, San Francisco
Skadsen J (2008) Distribution system on-line monitoring for detecting contamination and water quality changes. J AWWA 100:7
Story MS, Van der Gaag B, Burns B (2011) Advances in on-line drinking water quality monitoring and early warning systems. Water Res 45:741–747
Yang YJ, Haught RC, Goodrich JA (2009) Real-time contaminant detection and classification in a drinking water pipe using conventional water quality sensors: techniques and experimental results. J Environ Manage 90(8):2494–2506
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Appendix A
Appendix A
Following is a list of websites for common EDS systems
Gardian Blue by Hach: http://hachhst.com/products/cityguard-virtual-command-center
Canary by Sandia Labs: https://share.sandia.gov/news/resources/news_releases/canary/
Moni::tool by S::can: http://www.s-can.at/
BlueBox by WhiteWater: http://www.w-water.com/qualitysecurity/Product.aspx?id=64
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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. https://doi.org/10.1007/978-3-319-01092-2_4
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DOI: https://doi.org/10.1007/978-3-319-01092-2_4
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