Application of Artificial Neural Networks (ANNs) for the prediction of CSO discharges

Combined sewer overflows (CSOs) represent a common feature in combined urban drainage systems and are used to discharge excess water to the environment during heavy storms. Due to problems arising within the system, such as blockages, the release structures can unfortunately operate even in dry weather conditions spilling the polluted foul water directly to the watercourse. In order to prevent this from occurring, research regarding the prediction of the performance of CSO assets is being carried out in three UK catchments in collaboration with Yorkshire Water Systems Services. The overall strategy is to develop a monitoring, modelling and predictive operational strategy that utilizes rainfall input from weather radar to ultimately predict the hydraulic performance. Investigating an alternative approach to hydraulic models, an Artificial Neural Network (ANN) was utilised to predict the CSO performance. A three hiddenlayer feed-forward multilayer perceptron (MLP) was trained, validated and tested with data recorded from the system. Preliminary results from the first catchment suggest that the underlying relationship between local rainfall and water depth related to the weir crest within the CSO structure can be captured successfully in order to predict the normal CSO performance three time steps ahead.

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