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

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Authors
Fernando, Achela
Zhang, Xiujuan
Kinley, Peter F.
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Date
2006
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Conference Contribution - Paper in Published Proceedings
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Keyword
Artificial Neural Network (ANN)
back-propagation learning
forecasting
combined sewer overflows
ANZSRC Field of Research Code (2020)
Citation
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.
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.
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World Enformatika Society
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