Reference evapotranspiration forecasting by artificial neural networks
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1590/1809-4430-eng.agric.v37n6p1116-1125/2017 http://hdl.handle.net/11449/170395 |
Resumo: | Evapotranspiration (ET) is the main component of water balance in agricultural systems and the most active variable of the hydrological cycle. In the literature, few studies have used the forecast the day before via Artificial Neural Networks (ANNs) for the northern region of São Paulo state, Brazil. Therefore, this aimed to predict the reference evapotranspiration for Jaboticabal, the major sugarcane-producing region of São Paulo state. We used a historical series of data on average air temperature, wind speed, net radiation, soil heat flux, and daily relative humidity from 2002 to 2012, for Jaboticabal, SP (Brazil). ET was estimated by Penman-Monteith method. To forecast reference evapotranspiration, we used a feed-forward Multi-Layer Perceptron (MLP), which is a traditional Artificial Neural Network. Numerous topologies and variations were tested between neurons in intermediate and outer layers until the most accurate were obtained. We separated 75% from data for network training (2002 to 2010) and 25% for testing (2011 to 2013). The criteria for assessing the ANN performance were accuracy, precision, and trend. ET could be accurately estimated with a day to spare at any time of the year, by means of artificial neural networks, and using only air temperature data as an input variable. |
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Repositório Institucional da UNESP |
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Reference evapotranspiration forecasting by artificial neural networksAir temperatureEstimateModelingMulti-Layer PerceptronEvapotranspiration (ET) is the main component of water balance in agricultural systems and the most active variable of the hydrological cycle. In the literature, few studies have used the forecast the day before via Artificial Neural Networks (ANNs) for the northern region of São Paulo state, Brazil. Therefore, this aimed to predict the reference evapotranspiration for Jaboticabal, the major sugarcane-producing region of São Paulo state. We used a historical series of data on average air temperature, wind speed, net radiation, soil heat flux, and daily relative humidity from 2002 to 2012, for Jaboticabal, SP (Brazil). ET was estimated by Penman-Monteith method. To forecast reference evapotranspiration, we used a feed-forward Multi-Layer Perceptron (MLP), which is a traditional Artificial Neural Network. Numerous topologies and variations were tested between neurons in intermediate and outer layers until the most accurate were obtained. We separated 75% from data for network training (2002 to 2010) and 25% for testing (2011 to 2013). The criteria for assessing the ANN performance were accuracy, precision, and trend. ET could be accurately estimated with a day to spare at any time of the year, by means of artificial neural networks, and using only air temperature data as an input variable.São Paulo State University (Unesp) School of Agricultural and Veterinarian SciencesSão Paulo State University (Unesp) School of Agricultural and Veterinarian SciencesUniversidade Estadual Paulista (Unesp)Alves, Walison B. [UNESP]Rolim, Glauco De S. [UNESP]Aparecido, Lucas E. De O. [UNESP]2018-12-11T16:50:37Z2018-12-11T16:50:37Z2017-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article1116-1125application/pdfhttp://dx.doi.org/10.1590/1809-4430-eng.agric.v37n6p1116-1125/2017Engenharia Agricola, v. 37, n. 6, p. 1116-1125, 2017.1808-43890100-6916http://hdl.handle.net/11449/17039510.1590/1809-4430-eng.agric.v37n6p1116-1125/2017S0100-691620170006011162-s2.0-85034595803S0100-69162017000601116.pdfScopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengEngenharia Agricola0,305info:eu-repo/semantics/openAccess2023-10-04T06:02:04Zoai:repositorio.unesp.br:11449/170395Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T13:57:32.141630Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Reference evapotranspiration forecasting by artificial neural networks |
title |
Reference evapotranspiration forecasting by artificial neural networks |
spellingShingle |
Reference evapotranspiration forecasting by artificial neural networks Alves, Walison B. [UNESP] Air temperature Estimate Modeling Multi-Layer Perceptron |
title_short |
Reference evapotranspiration forecasting by artificial neural networks |
title_full |
Reference evapotranspiration forecasting by artificial neural networks |
title_fullStr |
Reference evapotranspiration forecasting by artificial neural networks |
title_full_unstemmed |
Reference evapotranspiration forecasting by artificial neural networks |
title_sort |
Reference evapotranspiration forecasting by artificial neural networks |
author |
Alves, Walison B. [UNESP] |
author_facet |
Alves, Walison B. [UNESP] Rolim, Glauco De S. [UNESP] Aparecido, Lucas E. De O. [UNESP] |
author_role |
author |
author2 |
Rolim, Glauco De S. [UNESP] Aparecido, Lucas E. De O. [UNESP] |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Alves, Walison B. [UNESP] Rolim, Glauco De S. [UNESP] Aparecido, Lucas E. De O. [UNESP] |
dc.subject.por.fl_str_mv |
Air temperature Estimate Modeling Multi-Layer Perceptron |
topic |
Air temperature Estimate Modeling Multi-Layer Perceptron |
description |
Evapotranspiration (ET) is the main component of water balance in agricultural systems and the most active variable of the hydrological cycle. In the literature, few studies have used the forecast the day before via Artificial Neural Networks (ANNs) for the northern region of São Paulo state, Brazil. Therefore, this aimed to predict the reference evapotranspiration for Jaboticabal, the major sugarcane-producing region of São Paulo state. We used a historical series of data on average air temperature, wind speed, net radiation, soil heat flux, and daily relative humidity from 2002 to 2012, for Jaboticabal, SP (Brazil). ET was estimated by Penman-Monteith method. To forecast reference evapotranspiration, we used a feed-forward Multi-Layer Perceptron (MLP), which is a traditional Artificial Neural Network. Numerous topologies and variations were tested between neurons in intermediate and outer layers until the most accurate were obtained. We separated 75% from data for network training (2002 to 2010) and 25% for testing (2011 to 2013). The criteria for assessing the ANN performance were accuracy, precision, and trend. ET could be accurately estimated with a day to spare at any time of the year, by means of artificial neural networks, and using only air temperature data as an input variable. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-01-01 2018-12-11T16:50:37Z 2018-12-11T16:50:37Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1590/1809-4430-eng.agric.v37n6p1116-1125/2017 Engenharia Agricola, v. 37, n. 6, p. 1116-1125, 2017. 1808-4389 0100-6916 http://hdl.handle.net/11449/170395 10.1590/1809-4430-eng.agric.v37n6p1116-1125/2017 S0100-69162017000601116 2-s2.0-85034595803 S0100-69162017000601116.pdf |
url |
http://dx.doi.org/10.1590/1809-4430-eng.agric.v37n6p1116-1125/2017 http://hdl.handle.net/11449/170395 |
identifier_str_mv |
Engenharia Agricola, v. 37, n. 6, p. 1116-1125, 2017. 1808-4389 0100-6916 10.1590/1809-4430-eng.agric.v37n6p1116-1125/2017 S0100-69162017000601116 2-s2.0-85034595803 S0100-69162017000601116.pdf |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Engenharia Agricola 0,305 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
1116-1125 application/pdf |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808128295470891008 |