Echo State Networks for Seasonal Streamflow Series Forecasting

• Published in: Intelligent Data Engineering and Automated Learning - IDEAL 2012 Vol. 7435; pp. 226 - 236
• Main Authors: Siqueira, Hugo, Boccato, Levy, Attux, Romis, Filho, Christiano Lyra
• Format: Book Chapter
• Language: English
• Published: Germany Springer Berlin / Heidelberg 2012; Springer Berlin Heidelberg
• Series: Lecture Notes in Computer Science
• Subjects: Echo State Networks; Forecasting; Monthly Seasonal Streamflow Series; PCA; Volterra Filtering
• ISBN: 3642326382; 9783642326387
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-642-32639-4_28

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.