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Building hydrological single-model ensembles using artificial neural networks and a combinatorial optimization approach

In recent years, the application of model ensembles has received increasing attention in the hydrological modelling community due to the interesting results reported in several studies carried out in different parts of the world. The main idea of these approaches is to combine the results of the same hydrological model or a number of different hydrological models in order to obtain more robust, better-fitting models, reducing at the same time the uncertainty in the predictions. The techniques for combining models range from simple approaches such as averaging different simulations, to more complex techniques such as least squares, genetic algorithms and more recently artificial intelligence techniques such as Artificial Neural Networks (ANN).