Generalizability of machine learning models and empirical equations for the estimation of reference evapotranspiration from temperature in a semiarid region
Autor(a) principal: | |
---|---|
Data de Publicação: | 2021 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Anais da Academia Brasileira de Ciências (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652021000101506 |
Resumo: | Abstract The Penman-Monteith equation is recommended for the estimation of reference evapotranspiration (ETo). However, it requires meteorological data that are commonly unavailable. Thus, this study evaluates artificial neural network (ANN), multivariate adaptive regression splines (MARS), and the original and calibrated Hargreaves-Samani (HS) and Penman-Monteith temperature (PMT) equations for the estimation of daily ETo using temperature. Two scenarios were considered: (i) local, models were calibrated/developed and evaluated using data from individual weather stations; (ii) regional, models were calibrated/developed using pooled data from several stations and evaluated independently in each one. Local models were also evaluated outside the calibration/training station. Data from 9 stations were used. The original PMT outperformed the original HS, but after local or regional calibrations, they performed similarly. The locally calibrated equations and the local machine learning models exhibited higher performances than their regional versions. However, the regional models had higher generalization capacity, with a more stable performance between stations. The machine learning models performed better than the equations evaluated. When comparing the ANN models with the HS equation, mean RMSE reduced from 0.96 to 0.87 and from 0.84 to 0.73, in regional and local scenarios, respectively. ANN and MARS performed similarly, with a slight advantage for ANN. |
id |
ABC-1_ec495bd19fd0c9aab8bd64c7b6e10140 |
---|---|
oai_identifier_str |
oai:scielo:S0001-37652021000101506 |
network_acronym_str |
ABC-1 |
network_name_str |
Anais da Academia Brasileira de Ciências (Online) |
repository_id_str |
|
spelling |
Generalizability of machine learning models and empirical equations for the estimation of reference evapotranspiration from temperature in a semiarid regionANNcross-stationexternal validationMARSregional modelsAbstract The Penman-Monteith equation is recommended for the estimation of reference evapotranspiration (ETo). However, it requires meteorological data that are commonly unavailable. Thus, this study evaluates artificial neural network (ANN), multivariate adaptive regression splines (MARS), and the original and calibrated Hargreaves-Samani (HS) and Penman-Monteith temperature (PMT) equations for the estimation of daily ETo using temperature. Two scenarios were considered: (i) local, models were calibrated/developed and evaluated using data from individual weather stations; (ii) regional, models were calibrated/developed using pooled data from several stations and evaluated independently in each one. Local models were also evaluated outside the calibration/training station. Data from 9 stations were used. The original PMT outperformed the original HS, but after local or regional calibrations, they performed similarly. The locally calibrated equations and the local machine learning models exhibited higher performances than their regional versions. However, the regional models had higher generalization capacity, with a more stable performance between stations. The machine learning models performed better than the equations evaluated. When comparing the ANN models with the HS equation, mean RMSE reduced from 0.96 to 0.87 and from 0.84 to 0.73, in regional and local scenarios, respectively. ANN and MARS performed similarly, with a slight advantage for ANN.Academia Brasileira de Ciências2021-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652021000101506Anais da Academia Brasileira de Ciências v.93 n.1 2021reponame:Anais da Academia Brasileira de Ciências (Online)instname:Academia Brasileira de Ciências (ABC)instacron:ABC10.1590/0001-3765202120200304info:eu-repo/semantics/openAccessFERREIRA,LUCAS B.CUNHA,FERNANDO F. DASILVA,GUSTAVO H. DACAMPOS,FLAVIO B.DIAS,SANTOS H.B.SANTOS,JANNAYTON E.O.eng2021-03-24T00:00:00Zoai:scielo:S0001-37652021000101506Revistahttp://www.scielo.br/aabchttps://old.scielo.br/oai/scielo-oai.php||aabc@abc.org.br1678-26900001-3765opendoar:2021-03-24T00:00Anais da Academia Brasileira de Ciências (Online) - Academia Brasileira de Ciências (ABC)false |
dc.title.none.fl_str_mv |
Generalizability of machine learning models and empirical equations for the estimation of reference evapotranspiration from temperature in a semiarid region |
title |
Generalizability of machine learning models and empirical equations for the estimation of reference evapotranspiration from temperature in a semiarid region |
spellingShingle |
Generalizability of machine learning models and empirical equations for the estimation of reference evapotranspiration from temperature in a semiarid region FERREIRA,LUCAS B. ANN cross-station external validation MARS regional models |
title_short |
Generalizability of machine learning models and empirical equations for the estimation of reference evapotranspiration from temperature in a semiarid region |
title_full |
Generalizability of machine learning models and empirical equations for the estimation of reference evapotranspiration from temperature in a semiarid region |
title_fullStr |
Generalizability of machine learning models and empirical equations for the estimation of reference evapotranspiration from temperature in a semiarid region |
title_full_unstemmed |
Generalizability of machine learning models and empirical equations for the estimation of reference evapotranspiration from temperature in a semiarid region |
title_sort |
Generalizability of machine learning models and empirical equations for the estimation of reference evapotranspiration from temperature in a semiarid region |
author |
FERREIRA,LUCAS B. |
author_facet |
FERREIRA,LUCAS B. CUNHA,FERNANDO F. DA SILVA,GUSTAVO H. DA CAMPOS,FLAVIO B. DIAS,SANTOS H.B. SANTOS,JANNAYTON E.O. |
author_role |
author |
author2 |
CUNHA,FERNANDO F. DA SILVA,GUSTAVO H. DA CAMPOS,FLAVIO B. DIAS,SANTOS H.B. SANTOS,JANNAYTON E.O. |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
FERREIRA,LUCAS B. CUNHA,FERNANDO F. DA SILVA,GUSTAVO H. DA CAMPOS,FLAVIO B. DIAS,SANTOS H.B. SANTOS,JANNAYTON E.O. |
dc.subject.por.fl_str_mv |
ANN cross-station external validation MARS regional models |
topic |
ANN cross-station external validation MARS regional models |
description |
Abstract The Penman-Monteith equation is recommended for the estimation of reference evapotranspiration (ETo). However, it requires meteorological data that are commonly unavailable. Thus, this study evaluates artificial neural network (ANN), multivariate adaptive regression splines (MARS), and the original and calibrated Hargreaves-Samani (HS) and Penman-Monteith temperature (PMT) equations for the estimation of daily ETo using temperature. Two scenarios were considered: (i) local, models were calibrated/developed and evaluated using data from individual weather stations; (ii) regional, models were calibrated/developed using pooled data from several stations and evaluated independently in each one. Local models were also evaluated outside the calibration/training station. Data from 9 stations were used. The original PMT outperformed the original HS, but after local or regional calibrations, they performed similarly. The locally calibrated equations and the local machine learning models exhibited higher performances than their regional versions. However, the regional models had higher generalization capacity, with a more stable performance between stations. The machine learning models performed better than the equations evaluated. When comparing the ANN models with the HS equation, mean RMSE reduced from 0.96 to 0.87 and from 0.84 to 0.73, in regional and local scenarios, respectively. ANN and MARS performed similarly, with a slight advantage for ANN. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-01-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652021000101506 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652021000101506 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/0001-3765202120200304 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Academia Brasileira de Ciências |
publisher.none.fl_str_mv |
Academia Brasileira de Ciências |
dc.source.none.fl_str_mv |
Anais da Academia Brasileira de Ciências v.93 n.1 2021 reponame:Anais da Academia Brasileira de Ciências (Online) instname:Academia Brasileira de Ciências (ABC) instacron:ABC |
instname_str |
Academia Brasileira de Ciências (ABC) |
instacron_str |
ABC |
institution |
ABC |
reponame_str |
Anais da Academia Brasileira de Ciências (Online) |
collection |
Anais da Academia Brasileira de Ciências (Online) |
repository.name.fl_str_mv |
Anais da Academia Brasileira de Ciências (Online) - Academia Brasileira de Ciências (ABC) |
repository.mail.fl_str_mv |
||aabc@abc.org.br |
_version_ |
1754302869749628928 |