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Despite theoretical benefits of recurrent artificial neural networks over their feedforward  counterparts, it is still unclear whether the former offer practical advantages as  rainfall–runoff models. The main drawback of recurrent networks is the increased complexity of  the training procedure due to their architecture. This work uses the recently introduced and conceptually  simple echo state networks for streamflow forecasts on twelve river basins in the  Eastern United States, and compares them to a variety of traditional feedforward and recurrent  approaches. Two modifications on the echo state network models are made that increase the  hydrologically relevant information content of their internal state. The results show that the  echo state networks outperform feedforward networks and are competitive with  state-of-the-art recurrent networks, across a range of performance measures. This, along with  their simplicity and ease of training, suggests that they can be considered  promising alternatives to traditional artificial neural networks in rainfall–runoff modelling

Echo state networks as an alternative to traditional artificial neural networks in rainfall–runoff modelling