SciELO - Scientific Electronic Library Online

 
vol.42 número3Composição química e capacidade antioxidante da polpa do café índice de autoresíndice de assuntospesquisa de artigos
Home Pagelista alfabética de periódicos  

Serviços Personalizados

Journal

Artigo

Indicadores

Links relacionados

Compartilhar


Ciência e Agrotecnologia

versão impressa ISSN 1413-7054versão On-line ISSN 1981-1829

Resumo

ALTHOFF, Daniel; BAZAME, Helizani Couto; FILGUEIRAS, Roberto  e  DIAS, Santos Henrique Brant. Heuristic methods applied in reference evapotranspiration modeling. Ciênc. agrotec. [online]. 2018, vol.42, n.3, pp.314-324. ISSN 1413-7054.  https://doi.org/10.1590/1413-70542018423006818.

The importance of the precise estimation of evapotranspiration is directly related to sustainable water usage. Since agriculture represents 70% of Brazil’s water consumption, adequate and efficient application of water may reduce the conflicts over the use of water among the multiple users. Considering the importance of accurate estimation of evapotranspiration, the objective of the present study was to model and compare the reference evapotranspiration from different heuristic methodologies. The standard Penman-Monteith method was used as reference for evapotranspiration, however, to evaluate the heuristic methodologies with scarce data, two widely known methods had their performances assessed in relation to Penman-Monteith. The methods used to estimate evapotranspiration from scarce data were Priestley-Taylor and Thornthwaite. The computational techniques Stepwise Regression (SWR), Random Forest (RF), Cubist (CB), Bayesian Regularized Neural Network (BRNN) and Support Vector Machines (SVM) were used to estimate evapotranspiration with scarce and full meteorological data. The results show the robustness of the heuristic methods in the prediction of the evapotranspiration. The performance criteria of machine learning methods for full weather data varied from 0.14 to 0.22 mm d-1 for mean absolute error (MAE), from 0.21 to 0.29 mm d-1 for root mean squared error (RMSE) and from 0.95 to 0.99 coefficient of determination (r²). The computational techniques proved superior performance to established methods in literature, even in scenarios of scarce variables. The BRNN presented the best performance overall.

Palavras-chave : Machine learning; model comparison; water management.

        · resumo em Português     · texto em Inglês     · Inglês ( pdf )