Abstract
The human-assisted application of machine learning techniques in the domain of water distribution networks is presented, corresponding to a research work done in the context of the European Esprit project WATERNET. One part of this project is a learning system that intends to capture knowledge from historic information collected during the operation of water distribution networks. The captured knowledge is expected to contribute to the improvement of the operation of the network. Presented ideas correspond to the first development phase of the learning system, focusing specially on the adopted methodology. The interactions between different classes of human experts and the learning system are also discussed. Finally some experimental results are presented.
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