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Real Time Prediction of WWTP Inflow Using Cascade Correlations and Genetic Algorithm ANN: Developing an Early Warning System for High Flows at Gold Bar WWTP

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Optimizing wastewater treatment through real-time control (RTC) of plant operation can aid in reducing the volume of untreated combined sewer overflow discharged into receiving waters. Therefore, adequate lead-time prediction of wastewater inflow is critical to successful RTC of different unit processes in a WWTP. This study presents an innovative forecasting tool that utilizes the capabilities of artificial neural networks (ANNs) for real-time predictions of wastewater inflow. A number of ANN models were developed to predict Gold Bar WWTP inflow 1-hr in advance, using inputs from rain gauges, flow monitors, and temperature probes. The validation results of this case study have shown that the ANN modeling tool is satisfactory(the onset of ≈80% and 70% of the rain and snow melting events were correctly captured by the models, respectively). The developed early warning system (EWS) was designed to accommodate equipment malfunctions by implementing 33 modelling algorithms within the EWS.

Keywords: Early warning system; cascade correlations; genetic algorithm; modeling; neural networks; wet weather flow

Document Type: Research Article


Publication date: January 1, 2010

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  • Proceedings of the Water Environment Federation is an archive of papers published in the proceedings of the annual Water Environment Federation® Technical Exhibition and Conference (WEFTEC® ) and specialty conferences held since the year 2000. These proceedings are not peer reviewed.

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