Portal de Periódicos da CAPES

Artigo Produção Nacional Revisado por pares

BIBTEX RIS

Estimating Evapotranspiration Using Artificial Neural Network and Minimum Climatological Data

2007; American Society of Civil Engineers; Volume: 133; Issue: 2 Linguagem: Inglês

10.1061/(asce)0733-9437(2007)133

1943-4774

Sidney Sára Zanetti, Elias Fernandes de Sousa, Vicente de Paulo Santos de Oliveira, Frederico Terra de Almeida, Sara Bernardo,

Solar Radiation and Photovoltaics

The objective of this study was to test an artificial neural network (ANN) for estimating the reference evapotranspiration (ETo) as a function of the maximum and minimum air temperatures in the Campos dos Goytacazes county, State of Rio de Janeiro. The data used in the network training were obtained from a historical series (September 1996 to August 2002) of daily climatic data collected in Campos dos Goytacazes county. When testing the artificial neural network, two historical series were used (September 2002 to August 2003) relative to Campos dos Goytacazes, and Viçosa, State of Minas Gerais. The ANNs (multilayer perceptron type) were trained to estimate ETo as a function of the maximum and minimum air temperatures, extraterrestrial radiation, and the daylight hours; and the last two were previously calculated as a function of either the local latitude or the Julian date. According to the results obtained in this ANN testing phase, it is concluded that when taking into account just the maximum and minimum air temperatures, it is possible to estimate ETo in Campos dos Goytacazes.