The optimal control of hydraulic networks often necessitates making a considerable number of very rapid simulations of flows, such as is not practical using existing, computationally-demanding, numerical-hydraulic models. However, the site-specific knowledge and data that is encapsulated in any such numerical model can be encapsulated in its turn in an artificial neural network (ANN), and this can provide much faster simulations. In this study, a number of possible types and configurations of ANNs are investigated for their suitability to this class of application. When regarded from a hydroinformatics point of view, this study becomes one of identifying the most suitable ANN encapsulations of numerical-hydraulic encapsulations of generic hydraulic knowledge and site-specific data.