Echo state networks for seasonal streamflow series forecasting

  • Authors:

  • Hugo Siqueira;Levy Boccato;Romis Attux;Christiano Lyra Filho

  • Affiliations:

  • Systems Engineering Department (DENSIS), University of Campinas --- UNICAMP, Campinas, SP, Brazil;Department of Computer Engineering and Industrial Automation (DCA), School of Electrical and Computer Engineering, University of Campinas --- UNICAMP, Campinas, SP, Brazil;Department of Computer Engineering and Industrial Automation (DCA), School of Electrical and Computer Engineering, University of Campinas --- UNICAMP, Campinas, SP, Brazil;Systems Engineering Department (DENSIS), University of Campinas --- UNICAMP, Campinas, SP, Brazil

  • Venue:
  • IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
  • Year:
  • 2012

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Abstract

The prediction of seasonal streamflow series is very important in countries where power generation is predominantly done by hydroelectric plants. Echo state networks can be safely regarded as promising tools in forecasting because they are recurrent networks that have a simple and efficient training process based on linear regression. Recently, Boccato et al. proposed a new architecture in which the output layer is built using a principal component analysis and a Volterra filter. This work performs a comparative investigation between the performances of different ESNs in the context of the forecasting of seasonal streamflow series associated with Brazilian hydroelectric plants. Two possible reservoir design approaches were tested with the classical and the Volterra-based output layer structures, and a multilayer perceptron was also included to establish bases for comparison. The obtained results show the relevance of these networks and also contribute to a better understanding of their applicability to forecasting problems.