Monthly runoff forecasting by means of artificial neural networks (ANNs).

Over the last decade or so, artificial neural networks (anns) have become one of the most promising tools for modelling hydrological processes such as rainfall runoff processes. however, the employment of a single model does not seem to be an appropriate approach for modelling such a complex, nonlinear, and discontinuous process thatvaries in space and time. for this reason, this study aims at decomposing the process into different clusters based on self-organizing map (som) ann approach, and thereafter modelling different clusters into outputs using separate feed-forward multilayer perceptron (mlp) and supervised self-organizing map (ssom) ann models. specifically,three different som models have been employed in order to cluster the input patterns into two, three, and four clusters respectively so that each cluster in each model corresponds to certain physics of the process under investigation and thereafter modelling of the input patterns in each cluster into corresponding outputs using feedforward mlp and ssom ann models. the employed models were developed on two different watersheds, iranian and canadian. it was found that although the idea of decomposition based on som is highly persuasive, our results indicate that there is a need for more principled procedure in order to decompose the process. moreover, according to the modelling results the ssom can be considered as an alternative approach to the feed-forward mlp.