The Milwaukee Metropolitan Sewerage District (MMSD) uses wet weather flow forecasts to aid in the effective operation of their wastewater conveyance and treatment system,. Historically, these forecasts have been based on predicted rainfall and antecedent moisture conditions. However, after seven years of operation it became apparent that poor flow forecasts were inadequate for effective system management A study, started in 2001, attempted to improve forecasts through the use of an Artificial Neural Network (ANN). Two options are available for constructing an ANN for the system, using historical system data or relying on data generated using a computer simulation model of the collection system. This paper describes the development and testing of several ANN models for forecasting flows in the MMSD conveyance system. The effectiveness of the selected model in reducing the frequency and volume of system overflows is also demonstrated.
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