Multivariate adaptive regression splines (MARS) applied to daily reference evapotranspiration modeling with limited weather data
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Acta Scientiarum. Agronomy (Online) |
Texto Completo: | http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/39880 |
Resumo: | 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 missing data and MARS. Four data availability scenarios were evaluated as follows: temperature only, temperature and solar radiation, temperature and relative humidity, and temperature and wind speed. The MARS models demonstrated superior performance in all scenarios. The models that used solar radiation showed the best performance, followed by those that used relative humidity and, finally, wind speed. The models based only on air temperature had the worst performance. |
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Acta Scientiarum. Agronomy (Online) |
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Multivariate adaptive regression splines (MARS) applied to daily reference evapotranspiration modeling with limited weather datadata drivenirrigation schedulingagrometeorologyartificial intelligence.AgrometeorologiaEstimation 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 missing data and MARS. Four data availability scenarios were evaluated as follows: temperature only, temperature and solar radiation, temperature and relative humidity, and temperature and wind speed. The MARS models demonstrated superior performance in all scenarios. The models that used solar radiation showed the best performance, followed by those that used relative humidity and, finally, wind speed. The models based only on air temperature had the worst performance.Universidade Estadual de Maringá2019-03-13info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/3988010.4025/actasciagron.v41i1.39880Acta Scientiarum. Agronomy; Vol 41 (2019): Publicação Contínua; e39880Acta Scientiarum. Agronomy; v. 41 (2019): Publicação Contínua; e398801807-86211679-9275reponame:Acta Scientiarum. Agronomy (Online)instname:Universidade Estadual de Maringá (UEM)instacron:UEMenghttp://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/39880/pdfCopyright (c) 2019 Acta Scientiarum. Agronomyhttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessFerreira, Lucas BorgesDuarte, Anunciene BarbosaCunha, Fernando França daFernandes Filho, Elpídio Inácio2019-09-24T12:25:27Zoai:periodicos.uem.br/ojs:article/39880Revistahttp://www.periodicos.uem.br/ojs/index.php/ActaSciAgronPUBhttp://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/oaiactaagron@uem.br||actaagron@uem.br|| edamasio@uem.br1807-86211679-9275opendoar:2019-09-24T12:25:27Acta Scientiarum. Agronomy (Online) - Universidade Estadual de Maringá (UEM)false |
dc.title.none.fl_str_mv |
Multivariate adaptive regression splines (MARS) applied to daily reference evapotranspiration modeling with limited weather data |
title |
Multivariate adaptive regression splines (MARS) applied to daily reference evapotranspiration modeling with limited weather data |
spellingShingle |
Multivariate adaptive regression splines (MARS) applied to daily reference evapotranspiration modeling with limited weather data Ferreira, Lucas Borges data driven irrigation scheduling agrometeorology artificial intelligence. Agrometeorologia |
title_short |
Multivariate adaptive regression splines (MARS) applied to daily reference evapotranspiration modeling with limited weather data |
title_full |
Multivariate adaptive regression splines (MARS) applied to daily reference evapotranspiration modeling with limited weather data |
title_fullStr |
Multivariate adaptive regression splines (MARS) applied to daily reference evapotranspiration modeling with limited weather data |
title_full_unstemmed |
Multivariate adaptive regression splines (MARS) applied to daily reference evapotranspiration modeling with limited weather data |
title_sort |
Multivariate adaptive regression splines (MARS) applied to daily reference evapotranspiration modeling with limited weather data |
author |
Ferreira, Lucas Borges |
author_facet |
Ferreira, Lucas Borges Duarte, Anunciene Barbosa Cunha, Fernando França da Fernandes Filho, Elpídio Inácio |
author_role |
author |
author2 |
Duarte, Anunciene Barbosa Cunha, Fernando França da Fernandes Filho, Elpídio Inácio |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Ferreira, Lucas Borges Duarte, Anunciene Barbosa Cunha, Fernando França da Fernandes Filho, Elpídio Inácio |
dc.subject.por.fl_str_mv |
data driven irrigation scheduling agrometeorology artificial intelligence. Agrometeorologia |
topic |
data driven irrigation scheduling agrometeorology artificial intelligence. Agrometeorologia |
description |
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 missing data and MARS. Four data availability scenarios were evaluated as follows: temperature only, temperature and solar radiation, temperature and relative humidity, and temperature and wind speed. The MARS models demonstrated superior performance in all scenarios. The models that used solar radiation showed the best performance, followed by those that used relative humidity and, finally, wind speed. The models based only on air temperature had the worst performance. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-03-13 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/39880 10.4025/actasciagron.v41i1.39880 |
url |
http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/39880 |
identifier_str_mv |
10.4025/actasciagron.v41i1.39880 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/39880/pdf |
dc.rights.driver.fl_str_mv |
Copyright (c) 2019 Acta Scientiarum. Agronomy https://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2019 Acta Scientiarum. Agronomy https://creativecommons.org/licenses/by/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Estadual de Maringá |
publisher.none.fl_str_mv |
Universidade Estadual de Maringá |
dc.source.none.fl_str_mv |
Acta Scientiarum. Agronomy; Vol 41 (2019): Publicação Contínua; e39880 Acta Scientiarum. Agronomy; v. 41 (2019): Publicação Contínua; e39880 1807-8621 1679-9275 reponame:Acta Scientiarum. Agronomy (Online) instname:Universidade Estadual de Maringá (UEM) instacron:UEM |
instname_str |
Universidade Estadual de Maringá (UEM) |
instacron_str |
UEM |
institution |
UEM |
reponame_str |
Acta Scientiarum. Agronomy (Online) |
collection |
Acta Scientiarum. Agronomy (Online) |
repository.name.fl_str_mv |
Acta Scientiarum. Agronomy (Online) - Universidade Estadual de Maringá (UEM) |
repository.mail.fl_str_mv |
actaagron@uem.br||actaagron@uem.br|| edamasio@uem.br |
_version_ |
1799305910576742400 |