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Combined sewer overflow forecasting with feed-forward back-propagation artificial neural network

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dc.contributor.author Fernando, Achela
dc.contributor.author Zhang, Xiujuan
dc.contributor.author Kinley, Peter F.
dc.date.accessioned 2012-06-20T21:02:48Z
dc.date.available 2012-06-20T21:02:48Z
dc.date.issued 2006
dc.identifier.uri http://hdl.handle.net/10652/1906
dc.description.abstract A 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.iso en en_NZ
dc.publisher World Enformatika Society en_NZ
dc.subject Artificial neural networks en_NZ
dc.subject Back-propagation learning en_NZ
dc.subject Forecasting en_NZ
dc.subject Combined sewer overflows en_NZ
dc.title Combined sewer overflow forecasting with feed-forward back-propagation artificial neural network en_NZ
dc.type Conference Contribution - Paper in Published Proceedings en_NZ
dc.rights.holder Authors en_NZ
dc.subject.marsden 090702 Environmental Engineering Modelling en_NZ
dc.identifier.bibliographicCitation Achela, 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.institution Unitec Institute of Technology en_NZ
unitec.institution Metrowater Ltd en_NZ
unitec.publication.spage 58 en_NZ
unitec.publication.lpage 64 en_NZ
unitec.publication.volume 12 en_NZ
unitec.publication.title Enformatika - International Transactions on Engineering, Computing, and Technology en_NZ
unitec.conference.title 12th International Conference on Computer Science en_NZ
unitec.conference.org World Enformatika Society en_NZ
unitec.conference.location Vienna en_NZ
unitec.conference.sdate 2006-03-29
unitec.conference.edate 2006-03-31
unitec.peerreviewed yes en_NZ


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