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Exploring the potential of machine learning to understand the occurrence and health risks of haloacetic acids in a drinking water distribution system.

• Authors : Yu Y; School of Environmental Science and Engineering, Xiamen University of Technology, Xiamen 361024, China; Drinking Water Science and Technology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China

Subjects: Machine Learning* ; Drinking Water*/Drinking Water*/Drinking Water*/chemistry ; Water Pollutants, Chemical*/Water Pollutants, Chemical*/Water Pollutants, Chemical*/analysis

• Source: The Science of the total environment [Sci Total Environ] 2024 Nov 15; Vol. 951, pp. 175573. Date of Electronic Publication: 2024 Aug 15.Publisher: Elsevier Country of Publication: Netherlands NLM ID: 0330500 Publication Model: Print-Electronic Cited Medium: Internet ISSN:

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The insightful water quality analysis and predictive model establishment via machine learning in dual-source drinking water distribution system.

• Authors : Li H; Key Laboratory of Yangtze River Water Environment, Ministry of Education, College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, China.; Zhou B

Subjects: Machine Learning* ; Water Quality* ; Drinking Water*/Drinking Water*/Drinking Water*/chemistry

• Source: Environmental research [Environ Res] 2024 Jun 01; Vol. 250, pp. 118474. Date of Electronic Publication: 2024 Feb 16.Publisher: Elsevier Country of Publication: Netherlands NLM ID: 0147621 Publication Model: Print-Electronic Cited Medium: Internet ISSN:

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A Novel Dynamic Fault Detection and Diagnosis Algorithm based on PCA and RF – An Application on a Water Distribution System in Northern Colombia.

• Authors : De-la-Cruz, Aldair; Portnoy, Ivan; Lombana, Antony

Subjects: FAULT diagnosis; WATER distribution; WATER quality

• Source: Procedia Computer Science; 2024, Vol. 251, p777-782, 6p

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Introducing Multi-Layer Concatenation as a Scheme to Combine Information in Water Distribution Cyber-Physical Systems.

• Authors : Vialatoux, Côme Frappé; Parrend, Pierre

Subjects: MACHINE learning; COMPUTER network traffic; WATER distribution

• Source: Procedia Computer Science; 2024, Vol. 246, p1840-1854, 15p

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Offline Learning with a Selection Hyper-Heuristic: An Application to Water Distribution Network Optimisation.

• Authors : Yates, William B. (AUTHOR); Keedwell, Edward C. (AUTHOR)

Subjects: WATER distribution; MACHINE learning; DIGITAL learning

• Source: Evolutionary Computation. Summer2021, Vol. 29 Issue 2, p187-210. 24p.

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Agricola

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Applied Science & Technology Full Text (H.W. Wilson)

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Agricola

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Applied Science & Technology Full Text (H.W. Wilson)

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Applied Science & Technology Full Text (H.W. Wilson)

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