Recent advances in knowledge extraction from neural network based hydrologic models

The paper describes some of the recent developments and applications for knowledge extraction from trained artificial neural network based hydrological models. The methods are in general derived from the casual relationship between inputs and outputs, and analysis of the hidden neuron behaviour. The discussions are focused on works related to explaining the internal behaviour and embedded physical process identification in a trained ANN rainfall-runoff model. It is envisaged that knowledge extraction is important for artificial neural networks to gain wider degree of acceptance especially in the context of hydrologic modeling. Therefore, this domain has to become a major field of research since it validates the use of neural networks for applications where reasons or explanations on why or how a result has been achieved are important. © 2009 Taylor & Francis Group, LLC.