Authors:
Luiz Biondi Neto
1
;
Pedro Henrique Gouvêa Coelho
1
;
Maria Luiza Fernandes Velloso
1
;
João Carlos C. B. Soares de Mello
2
and
Lidia Angulo Meza
3
Affiliations:
1
State University of Rio de Janeiro, Brazil
;
2
Fluminense Federal University, Brazil
;
3
Technology Science Institute, Veiga de Almeida University, Brazil
Keyword(s):
Time series estimation, Flow Estimation, Elman Neural Networks
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Enterprise Information Systems
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neural Network Software and Applications
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
This paper investigates the application of partially recurrent artificial neural networks (ANN) in the flow estimation for São Francisco River that feeds the hydroelectric power plant of Sobradinho. An Elman neural network was used suitably arranged to receive samples of the flow time series data available for São Francisco River shifted by one month. For that, the neural network input had a delay loop that included several sets of inputs separated in periods of five years monthly shifted. The considered neural network had three hidden layers. There is a feedback between the output and the input of the first hidden layer that enables the neural network to present temporal capabilities useful in tracking time variations. The data used in the application concern to the measured São Francisco river flow time series from 1931 to 1996, in a total of 65 years from what 60 were used for training and 5 for testing. The obtained results indicate that the Elman neural network is suitable to es
timate the river flow for 5 year periods monthly. The average estimation error was less than 0.2 %.
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