Predicting water quality for the woronora delivery network with sparse samples

Liang, B; Vitanage, D; Doolan, C; Li, Z; Taib, R; Mathews, G; Wang, Y; Lu, S; Chen, F; Hua, T; Peters, A

Predicting water quality for the woronora delivery network with sparse samples

Liang, B

Vitanage, D Doolan, C Li, Z

Taib, R Mathews, G Wang, Y Lu, S Chen, F

Hua, T Peters, A

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Publication Type:: Conference Proceeding
Citation:: Proceedings - IEEE International Conference on Data Mining, ICDM, 2019, 2019-November pp. 1210 - 1215
Issue Date:: 2019-11-01

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Field	Value	Language
dc.contributor.author	Liang, B https://orcid.org/0000-0002-6605-2167	en_US
dc.contributor.author	Vitanage, D	en_US
dc.contributor.author	Doolan, C	en_US
dc.contributor.author	Li, Z https://orcid.org/0000-0002-0784-157X	en_US
dc.contributor.author	Taib, R	en_US
dc.contributor.author	Mathews, G	en_US
dc.contributor.author	Wang, Y	en_US
dc.contributor.author	Lu, S	en_US
dc.contributor.author	Chen, F https://orcid.org/0000-0003-4971-8729	en_US
dc.contributor.author	Hua, T	en_US
dc.contributor.author	Peters, A	en_US
dc.date.accessioned	2020-04-20T09:29:52Z
dc.date.available	2020-04-20T09:29:52Z
dc.date.issued	2019-11-01	en_US
dc.identifier.citation	Proceedings - IEEE International Conference on Data Mining, ICDM, 2019, 2019-November pp. 1210 - 1215	en_US
dc.identifier.isbn	9781728146034	en_US
dc.identifier.issn	1550-4786	en_US
dc.identifier.uri	http://hdl.handle.net/10453/140120
dc.description.abstract	© 2019 IEEE. Monitoring drinking water quality in the entire delivery network, mainly indicated by total chlorine (TC), is a critical component of overall water supply management. However, it is extremely difficult to collect sufficient TC data from the network at customer sites, which makes it sparse for comprehensive modelling. This paper details an approach that provides TC prediction within the entire Woronora delivery network in Sydney in the next 24 hours. First, the hydraulic system is employed to capture the topology of the delivery network, so that the water travel time can be estimated using predicted water demand. The travel time links the upstream (reservoir) data to the downstream (resident) data. Then, a two-step strategy is proposed as a semi-parametric method to determine the crucial factors and build Bayesian model for TC decay to predict TC with the travel time. Lastly, the uncertainties of both data and the model are analysed to define the boundaries of prediction for better decision making. Several operational stages are involved when the approach is being deployed, including prediction interpretation, interactive tool development for water quality mapping and visualisation, and proactive optimisation. This has established a successful initiative to improve the overall water supply management for the entire Woronora delivery network.	en_US
dc.relation.ispartof	Proceedings - IEEE International Conference on Data Mining, ICDM	en_US
dc.relation.isbasedon	10.1109/ICDM.2019.00149	en_US
dc.title	Predicting water quality for the woronora delivery network with sparse samples	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2019-November	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	2019-November	en_US

Abstract:

© 2019 IEEE. Monitoring drinking water quality in the entire delivery network, mainly indicated by total chlorine (TC), is a critical component of overall water supply management. However, it is extremely difficult to collect sufficient TC data from the network at customer sites, which makes it sparse for comprehensive modelling. This paper details an approach that provides TC prediction within the entire Woronora delivery network in Sydney in the next 24 hours. First, the hydraulic system is employed to capture the topology of the delivery network, so that the water travel time can be estimated using predicted water demand. The travel time links the upstream (reservoir) data to the downstream (resident) data. Then, a two-step strategy is proposed as a semi-parametric method to determine the crucial factors and build Bayesian model for TC decay to predict TC with the travel time. Lastly, the uncertainties of both data and the model are analysed to define the boundaries of prediction for better decision making. Several operational stages are involved when the approach is being deployed, including prediction interpretation, interactive tool development for water quality mapping and visualisation, and proactive optimisation. This has established a successful initiative to improve the overall water supply management for the entire Woronora delivery network.

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http://hdl.handle.net/10453/140120