Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/22964
Citations
Scopus Web of Science® Altmetric
?
?
Full metadata record
DC FieldValueLanguage
dc.contributor.authorBowden, G.en
dc.contributor.authorNixon, J.en
dc.contributor.authorDandy, G.en
dc.contributor.authorMaier, H.en
dc.contributor.authorHolmes, M.en
dc.date.issued2006en
dc.identifier.citationMathematical and Computer Modelling, 2006; 44(5-6):469-484en
dc.identifier.issn0895-7177en
dc.identifier.urihttp://hdl.handle.net/2440/22964-
dc.descriptionCopyright © 2007 Elsevier B.Ven
dc.description.abstractIn a water distribution system (WDS), chlorine disinfection is important in preventing the spread of waterborne diseases. The ability to forecast chlorine residuals at strategic points in the WDS would be a significant aid to water quality managers in helping them to ensure the satisfaction and safety of their customers. In this research, general regression neural networks (GRNNs) are developed for forecasting chlorine residuals in the Myponga WDS, to the south of Adelaide, South Australia, up to 72 h in advance. A number of critical model issues are addressed including: the selection of an appropriate forecasting horizon; the division of the available data into subsets for modelling; and the determination of the inputs relevant to the chlorine forecasts. To determine if the GRNN is able to capture any nonlinear relationships that may be present in the data set, a comparison is made between the GRNN model and a multiple linear regression (MLR) model. Additional investigations are also performed to simulate the effects of a reduced sampling frequency, and to estimate model performance for longer lead-time forecasts. When tested on an independent validation set of data, the GRNN models are able to forecast chlorine levels to a high level of accuracy, up to 72 h in advance. The GRNN also significantly outperforms the MLR model, thereby providing evidence of the existence of nonlinear relationships in the data set.en
dc.description.statementofresponsibilityGavin J. Bowden, John B. Nixon, Graeme C. Dandy, Holger R. Maier and Mike Holmesen
dc.description.urihttp://www.elsevier.com/wps/find/journaldescription.cws_home/623/description#descriptionen
dc.language.isoenen
dc.publisherPergamon-Elsevier Science Ltden
dc.subjectArtificial neural networks; Forecasting; Chlorine residual; Water distribution system; General regression neural networken
dc.titleForecasting chlorine residuals in a water distribution system using a general regression neural networken
dc.typeJournal articleen
dc.identifier.rmid0020061135en
dc.identifier.doi10.1016/j.mcm.2006.01.006en
dc.identifier.pubid52487-
pubs.library.collectionCivil and Environmental Engineering publicationsen
pubs.verification-statusVerifieden
pubs.publication-statusPublisheden
dc.identifier.orcidDandy, G. [0000-0001-5846-7365]en
dc.identifier.orcidMaier, H. [0000-0002-0277-6887]en
Appears in Collections:Civil and Environmental Engineering publications
Environment Institute publications

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.