Reference evapotranspiration estimation with artificial neural networks

• Autor(a) principal: Patrícia Oliveira Lucas
• Data de Publicação: 2017
• Outros Autores: Renato Dourado Maia, Marcelo Rossi Vicente, Caio Vinicius Leite
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UFMG
• Texto Completo: http://hdl.handle.net/1843/42906
• topic: Evapotranspiração; Redes neurais (Computação); Irrigação agrícola; Solos - Umidade

Irrigation, when rationally used, can contribute to the efficient performance of the agribusiness. Planning irrigation, monitoring the soil moisture, the rainfall and the reference evapotranspiration (ETo) is necessary for a rational water management. The FAO PenmanMonteith (FAO PM) method is the standard method for estimating ETo, but in some cases, the use of this method is restricted due to missing some climatic variables. For this reason, methods with a lower number of meteorological variables, as temperature values, are quite often used. This study aims to propose an artificial neural network (ANN) to estimate the ETo as a function of maximum and minimum air temperatures for the city of Salinas, Minas Gerais State, Brazil. After training and validating the ANN, it was observed the existence of a good correlation between the values estimated by the standard method and those estimated by ANN, with the performance index classified as optimal. The use of ANN proved to be an excellent alternative for ETo estimation, reducing the costs of acquiring climatic data.