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Improving Water Quality Management With Artificial Intelligence.

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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]
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Database: Engineering Source
Description
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]
ISSN:0003150X
DOI:10.1002/awwa.70091