modeling and monthly mean streamflow forecasting in tocantins river, tucuruÍ hydroeletric power station, parÁ, amazon, brazil

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ID: 238133
2016
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Abstract
The hydroelectric power stations located in the Amazon are of extreme importance for a series of issues (environmental, social, economic) due to their impacts. This article describes and models the temporal series of monthly mean streamflow using stochastic models from SARIMA (seasonal autoregressive integrated moving average). The data was collected from august 2002 to august 2012 in Tucuruí Hydroelectric Power Station, Pará, Eastern Amazon, Brazil. Several SARIMA models were tested to describe the data. Akaike information criterion and goodness fit tests were used to choose the most parsimonious model. The chosen model to the streamflow series was SARIMA (0,0,2) (1,2,2)12, which adjusted to the observed series with Nash-Sutchiffe (CNS) coefficient 0.9150. This method, built with the observed data only, had great modeling and forecasting success.  Keywords: Hidrology; stochastic models; seasonality; SARIMA.
Reference Key
santos2016biotamodeling Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Paulo Guilherme Pinheiro dos Santos;Terezinha Ferreira de Oliveira
Journal lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics)
Year 2016
DOI
10.18561/2179-5746/biotaamazonia.v6n2p9-16
URL
https://periodicos.unifap.br/index.php/biota/article/view/1328
Keywords
Biology (General)

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