View in EDS

Improving Water Quality Management With Artificial Intelligence.

Saved in:
Bibliographic Details
Title: Improving Water Quality Management With Artificial Intelligence.
Authors: Burlingame, Gary A. (AUTHOR) gburlingame@verizon.net, Adams, Hunter (AUTHOR), Ganegoda, Sathya S. (AUTHOR), Dietrich, Andrea M. (AUTHOR) kikehata@txstate.edu
Source: Journal: American Water Works Association. Jun2026, Vol. 118 Issue 5, p56-59. 4p.
Subjects: Artificial intelligence, Water quality management, Machine learning, Water utilities, Water quality monitoring, Deep learning, Cyanobacterial blooms, Water quality
Abstract: The article focuses on the integration of artificial intelligence (AI) to enhance the monitoring and management of aesthetic water quality—attributes such as taste, odor, color, and clarity—in public water systems (PWSs). It highlights how AI, including machine learning and deep learning, can analyze real-time data from sensors and treatment processes to predict and address aesthetic issues, optimize treatment, and improve distribution system operations. The article also discusses challenges posed by changing source water conditions, such as cyanobacterial blooms, and the potential for AI-driven customer support tools to streamline consumer complaint management. Emphasizing that many PWSs are small or medium-sized with limited resources, the article suggests AI could provide accessible, data-driven decision support to ensure safe, compliant, and aesthetically acceptable drinking water across diverse systems. [Extracted from the article]
Copyright of Journal: American Water Works Association is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 193711069
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Improving Water Quality Management With Artificial Intelligence.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Burlingame%2C+Gary+A%2E%22">Burlingame, Gary A.</searchLink> (AUTHOR)<i> gburlingame@verizon.net</i><br /><searchLink fieldCode="AR" term="%22Adams%2C+Hunter%22">Adams, Hunter</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ganegoda%2C+Sathya+S%2E%22">Ganegoda, Sathya S.</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Dietrich%2C+Andrea+M%2E%22">Dietrich, Andrea M.</searchLink> (AUTHOR)<i> kikehata@txstate.edu</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Journal%3A+American+Water+Works+Association%22">Journal: American Water Works Association</searchLink>. Jun2026, Vol. 118 Issue 5, p56-59. 4p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Water+quality+management%22">Water quality management</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Water+utilities%22">Water utilities</searchLink><br /><searchLink fieldCode="DE" term="%22Water+quality+monitoring%22">Water quality monitoring</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Cyanobacterial+blooms%22">Cyanobacterial blooms</searchLink><br /><searchLink fieldCode="DE" term="%22Water+quality%22">Water quality</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The article focuses on the integration of artificial intelligence (AI) to enhance the monitoring and management of aesthetic water quality—attributes such as taste, odor, color, and clarity—in public water systems (PWSs). It highlights how AI, including machine learning and deep learning, can analyze real-time data from sensors and treatment processes to predict and address aesthetic issues, optimize treatment, and improve distribution system operations. The article also discusses challenges posed by changing source water conditions, such as cyanobacterial blooms, and the potential for AI-driven customer support tools to streamline consumer complaint management. Emphasizing that many PWSs are small or medium-sized with limited resources, the article suggests AI could provide accessible, data-driven decision support to ensure safe, compliant, and aesthetically acceptable drinking water across diverse systems. [Extracted from the article]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal: American Water Works Association is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=193711069
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1002/awwa.70091
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 4
        StartPage: 56
    Subjects:
      – SubjectFull: Artificial intelligence
        Type: general
      – SubjectFull: Water quality management
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Water utilities
        Type: general
      – SubjectFull: Water quality monitoring
        Type: general
      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: Cyanobacterial blooms
        Type: general
      – SubjectFull: Water quality
        Type: general
    Titles:
      – TitleFull: Improving Water Quality Management With Artificial Intelligence.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Burlingame, Gary A.
      – PersonEntity:
          Name:
            NameFull: Adams, Hunter
      – PersonEntity:
          Name:
            NameFull: Ganegoda, Sathya S.
      – PersonEntity:
          Name:
            NameFull: Dietrich, Andrea M.
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 0003150X
          Numbering:
            – Type: volume
              Value: 118
            – Type: issue
              Value: 5
          Titles:
            – TitleFull: Journal: American Water Works Association
              Type: main
ResultId 1