Data-driven water quality prediction in chloraminated systems

Peters, A; Liang, B; Tian, H; Li, Z; Doolan, C; Vitanage, D; Norris, H; Simpson, K; Wang, Y; Chen, F

Data-driven water quality prediction in chloraminated systems

Peters, A Liang, B

Tian, H Li, Z Doolan, C Vitanage, D Norris, H Simpson, K Wang, Y Chen, F

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Publisher:: Australian Water Association https://watersource.awa.asn.au/wp-content/uploads/2020/12/WEJ_2020_022.pdf
Publication Type:: Journal Article
Citation:: Water e-Journal, 2020, 5, (4), pp. 1-19
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Peters, A
dc.contributor.author	Liang, B https://orcid.org/0000-0002-6605-2167
dc.contributor.author	Tian, H
dc.contributor.author	Li, Z
dc.contributor.author	Doolan, C
dc.contributor.author	Vitanage, D
dc.contributor.author	Norris, H
dc.contributor.author	Simpson, K
dc.contributor.author	Wang, Y
dc.contributor.author	Chen, F
dc.date.accessioned	2021-02-05T21:30:22Z
dc.date.available	2021-02-05T21:30:22Z
dc.date.issued	2020-01-01
dc.identifier.citation	Water e-Journal, 2020, 5, (4), pp. 1-19
dc.identifier.issn	2206-1991
dc.identifier.issn	2206-1991
dc.identifier.uri	http://hdl.handle.net/10453/145871
dc.description.abstract	<jats:p>This paper proposes a data-driven method that provides water quality prediction within the entire Woronora delivery system in Sydney. Specifically, the key factors relating to water quality are identified through factor analysis. A Bayesian parametric decay model is formulated using the key factors to predict water quality. To estimate the water travel time, which links the upstream (reservoir) data to the downstream (resident) data, the hydraulic system is employed to capture the topology of the delivery system. Moreover, the uncertainties of both data and the model are analysed to define the boundaries of prediction for better decision making.</jats:p>
dc.language	en
dc.publisher	Australian Water Association https://watersource.awa.asn.au/wp-content/uploads/2020/12/WEJ_2020_022.pdf
dc.relation	Veolia Water Solutions & Technologies (Australia) Pty Ltd
dc.relation.ispartof	Water e-Journal
dc.relation.isbasedon	10.21139/wej.2020.022
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.title	Data-driven water quality prediction in chloraminated systems
dc.type	Journal Article
utslib.citation.volume	5
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/A/DRsch The Data Science Institute
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	in_progress	*
dc.date.updated	2021-02-05T21:29:54Z
pubs.issue	4
pubs.publication-status	Published online
pubs.volume	5
utslib.citation.issue	4

Abstract:

This paper proposes a data-driven method that provides water quality prediction within the entire Woronora delivery system in Sydney. Specifically, the key factors relating to water quality are identified through factor analysis. A Bayesian parametric decay model is formulated using the key factors to predict water quality. To estimate the water travel time, which links the upstream (reservoir) data to the downstream (resident) data, the hydraulic system is employed to capture the topology of the delivery system. Moreover, the uncertainties of both data and the model are analysed to define the boundaries of prediction for better decision making.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/145871