Name
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 estimate the river flow for 5 year periods monthly. The average estimation error was less than 0.2 %. |
Year | Venue | Keywords |
|---|---|---|
2004 | ICEIS (2) | flow estimation,elman neural networks,time series estimation,neural network,power plant,time series,time series data |
Field | DocType | Citations |
Streamflow,Data mining,Feedforward neural network,Computer science,Flow estimation,Probabilistic neural network,Flow time,Time delay neural network,Types of artificial neural networks,Artificial neural network | Conference | 1 |
PageRank | References | Authors |
0.43 | 4 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Luiz Biondi Neto | 1 | 8 | 6.33 |
| Pedro Henrique Gouvea Coelho | 2 | 5 | 7.74 |
| Maria Luiza F. Velloso | 3 | 12 | 5.93 |
| João Carlos Correia Baptista Soares De Mello | 4 | 108 | 14.67 |
| Lidia Angulo Meza | 5 | 92 | 9.81 |