Artificial neural networks have been used successfully in a number of areas of civil engineering, including hydrology and water resources engineering. In the vast majority of cases, multilayer perceptrons that are trained with the back-propagation algorithm are used. One of the major shortcomings of this approach is that it is difficult to elicit the knowledge about the input/output mapping that is stored in the trained networks. One way to overcome this problem is to use B-spline associative memory networks (AMNs), because their connection weights may be interpreted as a set of fuzzy membership functions and hence the relationship between the model inputs and outputs may be written as a set of fuzzy rules. In this paper, multilayer percept...