Artificial neural networks as a tool in urban storm drainage

The introduction of Artificial Neural Networks (ANNs) as a tool in the field of urban storm drainage is discussed. Besides some basic theory on the mechanics of ANNs and a general classification of the different types of ANNs, two ANN application examples are presented: The prediction of runoff coefficients and the restoration of rainfall data. From the results, it can be concluded that ANNs can deal with problems that are traditionally difficult for conventional modelling techniques to solve. Their advantages include good generalisation abilities, high fault tolerance, high execution speed, and the ability to adapt and learn. However, ANNs rely strongly on the quantity of data examples, their training is occasionally slow, and they are not transparent and obstruct any closer analysis and interpretation of their performance. Finally, it is expected that the future of ANNs will lie in its integration with other conventional and more advanced modelling techniques, creating so-called hybrid models. (C) 1997 IAWQ. Published by Elsevier Science Ltd.