Paper Info
Title
Neural networks for inflow forecasting using precipitation information
Abstract
This work presents forecast models for the natural inflow in the Basin of Iguaçu River, incorporating rainfall information, based on artificial neural networks. Two types of rainfall data are available: measurements taken from stations distributed along the basin and ten-day rainfall forecasts using the ETA model developed by CPTEC (Brazilian Weather Forecating Center). The neural nework model also employs observed inflows measured by stations along the Iguaçu River, as well as historical data of the natural inflows to be predicted. Initially, we applied preprocessing methods on the various series, filling missing data and correcting outliers. This was followed by methods for selecting the most relevant variables for the forecast model. The results obtained demonstrate the potential of using artificial neural networks in this problem, which is highly non-linear and very complex, providing forecasts with good accuracy that can be used in planning the hydroelectrical operation of the Basin.
Year
DOI
Venue
2007
10.1007/978-3-540-73325-6_55
IEA/AIE
Keywords
Field
DocType
inflow forecasting,eta model,rainfall information,historical data,neural nework model,precipitation information,forecast model,natural inflow,u river,missing data,rainfall data,artificial neural network,artificial neural networks,neural network
Meteorology,Outlier,Preprocessor,Environmental science,Missing data,Inflow,Artificial neural network,Precipitation types,Structural basin,Precipitation
Conference
Volume
ISSN
Citations 
4570
0302-9743
0
PageRank 
References 
Authors
0.34
2
6
Name
Order
Citations
PageRank
Karla Figueiredo1306.53
Carlos R. Hall Barbosa2435.14
André V. A. Da Cruz300.34
Marley B. R. Vellasco428047.47
Marco Aurélio C. Pacheco5407.05
Roxana J. Conteras600.34