Moving hydraulic jump could be regarded as a special case in unsteady flow, which changes the flow regime and generates a hydraulic discontinuity along the channel. Owing to the practical importance, the knowledge of the flow behavior within a reach that retains this phenomenon is quite essential for river flow routing and the management of flow distribution in a network. However, in spite of its practical importance, experimental data are scarce and the application of numerical simulation due to the presence of hydraulic discontinuity is, also, rather complicated. In such circumstances, compiling experimental data and analyzing them by using intelligent systems aid in distinguishing the parameters that influence the phenomenon most. In this research, data of unsteady flow compiled from a rectangular flume were analyzed by compiled using Artificial Neural Network and an integrated algorithm created from ANN and Genetic Algorithm, to optimize ANN parameters. During the experimental stage, variety of moving hydraulic jump conditions were produced by generating different hydrographs at the upstream end of the flume. Then, the perceptron ANN pattern and integrated algorithm, ANN- GA, were applied to the compiled data. The results indicated that the ANN, or the integrated algorithm which is an enhanced alternative approach, could be used as a useful means to estimate flow parameters in such complex flow conditions. Therefore employing the integration of ANN and GA to evaluate more experimental and field data, seems to build up the required knowledge to provide a simple yet reliable approach for flow routing and flow distribution management in water distribution networks.
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