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Using Data Mining to Understand Drinking Water Advisories in Small Water Systems: a Case Study of Ontario First Nations Drinking Water Supplies

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  • Richard Harvey
  • Heather Murphy
  • Edward McBean
  • Bahram Gharabaghi

Abstract

Although access to safe drinking water is widely assumed to be universal, small drinking water systems in many countries continue to experience an unacceptably large number of drinking water advisories (DWAs). The goal of this research is to describe novel data mining tools that identify the factors contributing to DWAs in small drinking water systems. A dataset containing information related to First Nations drinking water systems in the Province of Ontario, Canada is used for the case study. A decision tree classifier (one of the fastest and most versatile predictive modeling algorithms currently available for data mining) visually maps out the relationship of system characteristics (e.g., source water, system age, and operator certification) to DWA likelihood. The developed model achieves an overall accuracy of 71 % during repeated cross-validation of predictive performance and is of utility when prioritizing future expenditures aimed at proactively reducing the risk of delivering compromised water. Copyright Springer Science+Business Media Dordrecht 2015

Suggested Citation

  • Richard Harvey & Heather Murphy & Edward McBean & Bahram Gharabaghi, 2015. "Using Data Mining to Understand Drinking Water Advisories in Small Water Systems: a Case Study of Ontario First Nations Drinking Water Supplies," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(14), pages 5129-5139, November.
    • Handle: RePEc:spr:waterr:v:29:y:2015:i:14:p:5129-5139
    DOI: 10.1007/s11269-015-1108-6
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    References listed on IDEAS

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    1. Curriero, F.C. & Patz, J.A. & Rose, J.B. & Lele, S., 2001. "The association between extreme precipitation and waterborne disease outbreaks in the United States, 1948-1994," American Journal of Public Health, American Public Health Association, vol. 91(8), pages 1194-1199.
    2. Roula Khadra & Nicola Lamaddalena, 2010. "Development of a Decision Support System for Irrigation Systems Analysis," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(12), pages 3279-3297, September.
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    Cited by:

    1. Christine J. Kirchhoff & Julia A. Flagg & Yan Zhuang & Berdakh Utemuratov, 2019. "Understanding and Improving Enforcement and Compliance with Drinking Water Standards," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(5), pages 1647-1663, March.
    2. S. R. Mounce & K. Ellis & J. M. Edwards & V. L. Speight & N. Jakomis & J. B. Boxall, 2017. "Ensemble Decision Tree Models Using RUSBoost for Estimating Risk of Iron Failure in Drinking Water Distribution Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(5), pages 1575-1589, March.

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