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Ferreira, Lucas Borges; Duarte, Anunciene Barbosa; Cunha, Fernando França da; Fernandes Filho, Elpídio Inácio. |
Estimation of reference evapotranspiration (ETo) is very relevant for water resource management. The Penman-Monteith (PM) equation was proposed by the Food and Agriculture Organization (FAO) as the standard method for estimation of ETo. However, this method requires various weather data, such as air temperature, wind speed, solar radiation and relative humidity, which are often unavailable. Thus, the objective of this study was to compare the performance of multivariate adaptive regression splines (MARS) and alternative equations, in their original and calibrated forms, to estimate daily ETo with limited weather data. Daily data from 2002 to 2016 from 8 Brazilian weather stations were used. ETo was estimated using empirical equations, PM equation with... |
Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Data driven; Irrigation scheduling; Agrometeorology; Artificial intelligence.; Agrometeorologia. |
Ano: 2019 |
URL: http://periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/39880 |
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Ferreira, Lucas Borges; Duarte, Anunciene Barbosa; Cunha, Fernando França da; Fernandes Filho, Elpídio Inácio. |
Estimation of reference evapotranspiration (ETo) is very relevant for water resource management. The Penman-Monteith (PM) equation was proposed by the Food and Agriculture Organization (FAO) as the standard method for estimation of ETo. However, this method requires various weather data, such as air temperature, wind speed, solar radiation and relative humidity, which are often unavailable. Thus, the objective of this study was to compare the performance of multivariate adaptive regression splines (MARS) and alternative equations, in their original and calibrated forms, to estimate daily ETo with limited weather data. Daily data from 2002 to 2016 from 8 Brazilian weather stations were used. ETo was estimated using empirical equations, PM equation with... |
Tipo: Info:eu-repo/semantics/article |
Palavras-chave: 5.01.05.00-0 data driven; Irrigation scheduling; Agrometeorology; Artificial intelligence. Agrometeorologia. |
Ano: 2019 |
URL: http://periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/39880 |
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