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dc.contributor.authorFernando, Achela
dc.contributor.authorZhang, Xiujuan
dc.contributor.authorKinley, Peter F.
dc.date.accessioned2012-06-20T21:02:48Z
dc.date.available2012-06-20T21:02:48Z
dc.date.issued2006
dc.identifier.urihttp://hdl.handle.net/10652/1906
dc.description.abstractA feed-forward, back-propagation Artificial Neural Network (ANN) model has been used to forecast the occurrences of wastewater overflows in a combined sewerage reticulation system. This approach was tested to evaluate its applicability as a method alternative to the common practice of developing a complete conceptual, mathematical hydrological-hydraulic model for the sewerage system to enable such forecasts. The ANN approach obviates the need for a-priori understanding and representation of the underlying hydrological hydraulic phenomena in mathematical terms but enables learning the characteristics of a sewer overflow from the historical data. The performance of the standard feed-forward, back-propagation of error algorithm was enhanced by a modified data normalizing technique that enabled the ANN model to extrapolate into the territory that was unseen by the training data. The algorithm and the data normalizing method are presented along with the ANN model output results that indicate a good accuracy in the forecasted sewer overflow rates. However, it was revealed that the accurate forecasting of the overflow rates are heavily dependent on the availability of a real-time flow monitoring at the overflow structure to provide antecedent flow rate data. The ability of the ANN to forecast the overflow rates without the antecedent flow rates (as is the case with traditional conceptual reticulation models) was found to be quite poor.en_NZ
dc.language.isoenen_NZ
dc.publisherWorld Enformatika Societyen_NZ
dc.subjectartificial neural networks (ANN)en_NZ
dc.subjectback-propagation learningen_NZ
dc.subjectforecastingen_NZ
dc.subjectcombined sewer overflowsen_NZ
dc.titleCombined sewer overflow forecasting with feed-forward back-propagation artificial neural networken_NZ
dc.typeConference Contribution - Paper in Published Proceedingsen_NZ
dc.rights.holderAuthorsen_NZ
dc.subject.marsden090702 Environmental Engineering Modellingen_NZ
dc.identifier.bibliographicCitationAchela, F., Zhang, X., & Kinley, P. (2006). Combined sewer overflow forecasting with feed-forward back-propagation artificial neural network. Enformatika - International Transactions on Engineering, Computing, and Technology, 12, 58-64.en_NZ
unitec.institutionUnitec Institute of Technologyen_NZ
unitec.institutionMetrowater Ltden_NZ
unitec.publication.spage58en_NZ
unitec.publication.lpage64en_NZ
unitec.publication.volume12en_NZ
unitec.publication.titleEnformatika - International Transactions on Engineering, Computing, and Technologyen_NZ
unitec.conference.title12th International Conference on Computer Scienceen_NZ
unitec.conference.orgWorld Enformatika Societyen_NZ
unitec.conference.locationViennaen_NZ
unitec.conference.sdate2006-03-29
unitec.conference.edate2006-03-31
unitec.peerreviewedyesen_NZ


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