This research focused on the application of artificial neural networks for flood prediction in ungauged catchments. Catchment descriptors were used as input data and the index flood was the output of the model. Different types and numbers of catchment descriptors (17 descriptors and more than 1000 catchments) were used to choose those that gave the best relationship with the hydrological behaviour and flood magnitude. ANN models with different architectures were developed and applied to training and validation sets of data to find the best type of ANN for this application. Selection of pooling groups of catchments either randomly or according to geographical proximity did not produce desirable results. Therefore hydrologically similar catchments were clustered using the WINFAP-FEH Software before entering descriptors into the ANN model. This improved the accuracy of predicted floods.

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