Is an artificial neural network (ANN) a reliable alternative to distributed models for combined sewer overflow (CSO) simulation?

The study compares two approaches in urban hydrology modelling: distributed models, which require detailed knowledge of sewer networks, and conceptual models using artificial neural networks that only need reliable data. The research specifically evaluated a Nonlinear AutoRegressive eXogenous (NARX) neural network model against a traditional distributed model for rainfall/discharge overflow prediction. Both models underwent identical calibration and validation processes. Results demonstrated that the NARX model achieved similar performance levels to the distributed model across various metrics, including overflow volume, peak discharge, and different efficiency measures (Nash–Sutcliffe efficiency, logarithmic Nash–Sutcliffe Efficiency, and Kling–Gupta Efficiency). Analysis of the 20 largest overflow events showed that the neural network accurately predicted overflow volumes in 75% of cases, compared to 60% for the distributed model. Performance criteria were met in nine out of 20 events for the neural network and 11 out of 20 for the distributed model. The study concludes that conceptual models, which are easier and more cost-effective to implement, can perform comparably to traditional distributed models in urban hydrology applications.