Abstract :
In recent years, artificial neural networks (ANNs) have become one of the most promising tools in order to model complex hydrological processes such as the rainfall-runoff process. In many studies, ANNs have demonstrated superior results compared to alternative methods. ANNs are able to map underlying relationship between input and output data without prior understanding of the process under investigation. However, they have been known as black-box models due to their problem in providing insight into the relationship learned. In this study, firstly we develop a rainfall-runoff model using an ANN approach, and secondly we describe different approaches, including Neural Interpretation Diagram, Garson’s algorithm, and randomization approach, for understanding the relationship learned by the ANN model. The results indicate that ANNs are promising tools not only in accurate modelling of complex processes but also in providing insight from the learned relationship which would assist the modeller in understanding of the process under investigation as well as in evaluation of the model.
