Data-mining methods predict chlorine residuals in premise plumbing using low-cost sensors

Daniella Saetta, Rain Richard, Carlos Leyva, Paul Westerhoff, Treavor H. Boyer

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


Variable water quality within buildings is of increasing concern due to public health impacts (e.g., lead, Legionella pneumophila, Naegleria fowleri, disinfection byproducts). Advances in data acquisition and analytics provide the opportunity to monitor real-time building-wide water quality variability. Accordingly, the goal of this research was to create a water quality sensor platform including data acquisition, storage, and mining methods able to monitor, and ultimately improve, water quality within buildings. The platform was used to monitor water temperature, pH, conductivity, oxidation–reduction potential, dissolved oxygen, and chlorine using sensors only. Other building data infrastructure, specifically Wi-Fi logins by occupants, were used to approximate activity rates and associated water use. An advanced machine-learning technique, gradient boosting machines, predicted the chlorine residuals throughout the building plumbing network better than multivariate linear regression models. Finally, the implications of water quality monitoring on costs, scalability, reliability, human dimensions, regulatory compliance, and future green building designs are considered.

Original languageEnglish (US)
Article numbere1214
JournalAWWA Water Science
Issue number1
StatePublished - Jan 1 2021


  • Water quality
  • chlorine
  • data
  • machine learning
  • premise plumbing
  • sensors

ASJC Scopus subject areas

  • Oceanography
  • Water Science and Technology
  • Ocean Engineering
  • Waste Management and Disposal


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