Título: Multivariate Modelling of Water Resources Time Series Using Constructive Neural Networks
Autores: Valença, Mêuser; Ludermir, Teresa
Resumo: This paper presents a constructive neural network model for daily streamflow forecasting. The Surface water hydrology is basic to the design and operation of the reservoir. A good example is the operation of a reservoir with an uncontrolled inflow but having a means of regulating the outflow. If information on the nature of the inflow is determinable in advance, then the reservoir can be operated by some decision rule to minimize downstream flood damage. For this reasons, several companies in the Brazilian Electrical Sector use the linear time-series models such as PARMA (Periodic Auto regressive Moving Average) models developed by Box-Jenkins. This paper provides for river flow prediction a numerical comparison between neural networks, called non-linear sigmoidal regression Blocks networks (NSRBN), and PARMA models. The NSRBN model approach is shown to provide better representation of the daily average water inflow forecasting, than the models based on Box-Jenkins method, currently in use on the Brazilian Electrical Sector.
Código DOI: 10.21528/CBRN2001-041
Artigo em pdf: 5cbrn_041.pdf
Arquivo BibTex: 5cbrn_041.bib