Predictive models of minimum temperatures for the south of Buenos Aires province

Autores
Hernandez, Gabriela Lorena; Muller, Gabriela Viviana; Villacampa, Yolanda; Navarro González, Francisco José; Aragonés, Luis
Año de publicación
2020
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Depending on the time of development of a crop temperature below 0 °C can cause damage to the plant, altering its development and subsequent yield. Since frosts are identified from the minimum air temperature, the objective of this research paper is to generate forecast -(predictive) models at 1, 3 and 5 days of the minimum daily temperature (Tmin) for Bahía Blanca city. Non-linear numerical models are generated using artificial neural networks and geometric models of finite elements. Six independent variables are used: temperature and dew point temperature at meteorological shelter level, relative humidity, cloudiness observed above the station, wind speed and direction measured at 10 m altitude. Data have been obtained between May and September from 1956 to 2015. Once the available data had been analyzed, this period was reduced to 2007–2015. For the selection of the most suitable model, the correlation coefficient of Pearson (R), the determination coefficient (R2) and the Mean Absolute Error (MAE) are evaluated. The results of the study determine that the geometric model of finite elements with 4 variables, over 9 years (2007–2015) and separated by the season of the year is the one that presents better adjustment in the forecast of Tmin with up to 5 days of anticipation.
Fil: Hernandez, Gabriela Lorena. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Agronomía; Argentina
Fil: Muller, Gabriela Viviana. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Centro de Estudios de Variabilidad y Cambio Climático; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina
Fil: Villacampa, Yolanda. Universidad de Alicante; España
Fil: Navarro González, Francisco José. Universidad de Alicante; España
Fil: Aragonés, Luis. Universidad de Alicante; España
Materia
AGRICULTURE
CROP TEMPERATURE
FINITE ELEMENTS
PREDICTIVE MODELS
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/149607
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Predictive models of minimum temperatures for the south of Buenos Aires provinceHernandez, Gabriela LorenaMuller, Gabriela VivianaVillacampa, YolandaNavarro González, Francisco JoséAragonés, LuisAGRICULTURECROP TEMPERATUREFINITE ELEMENTSPREDICTIVE MODELShttps://purl.org/becyt/ford/1.5https://purl.org/becyt/ford/1Depending on the time of development of a crop temperature below 0 °C can cause damage to the plant, altering its development and subsequent yield. Since frosts are identified from the minimum air temperature, the objective of this research paper is to generate forecast -(predictive) models at 1, 3 and 5 days of the minimum daily temperature (Tmin) for Bahía Blanca city. Non-linear numerical models are generated using artificial neural networks and geometric models of finite elements. Six independent variables are used: temperature and dew point temperature at meteorological shelter level, relative humidity, cloudiness observed above the station, wind speed and direction measured at 10 m altitude. Data have been obtained between May and September from 1956 to 2015. Once the available data had been analyzed, this period was reduced to 2007–2015. For the selection of the most suitable model, the correlation coefficient of Pearson (R), the determination coefficient (R2) and the Mean Absolute Error (MAE) are evaluated. The results of the study determine that the geometric model of finite elements with 4 variables, over 9 years (2007–2015) and separated by the season of the year is the one that presents better adjustment in the forecast of Tmin with up to 5 days of anticipation.Fil: Hernandez, Gabriela Lorena. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Agronomía; ArgentinaFil: Muller, Gabriela Viviana. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Centro de Estudios de Variabilidad y Cambio Climático; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; ArgentinaFil: Villacampa, Yolanda. Universidad de Alicante; EspañaFil: Navarro González, Francisco José. Universidad de Alicante; EspañaFil: Aragonés, Luis. Universidad de Alicante; EspañaElsevier2020-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/149607Hernandez, Gabriela Lorena; Muller, Gabriela Viviana; Villacampa, Yolanda; Navarro González, Francisco José; Aragonés, Luis; Predictive models of minimum temperatures for the south of Buenos Aires province; Elsevier; Science of the Total Environment; 699; 1-2020; 1-420048-9697CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/abs/pii/S0048969719342639info:eu-repo/semantics/altIdentifier/doi/10.1016/j.scitotenv.2019.134280info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:14:21Zoai:ri.conicet.gov.ar:11336/149607instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:14:22.262CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Predictive models of minimum temperatures for the south of Buenos Aires province
title Predictive models of minimum temperatures for the south of Buenos Aires province
spellingShingle Predictive models of minimum temperatures for the south of Buenos Aires province
Hernandez, Gabriela Lorena
AGRICULTURE
CROP TEMPERATURE
FINITE ELEMENTS
PREDICTIVE MODELS
title_short Predictive models of minimum temperatures for the south of Buenos Aires province
title_full Predictive models of minimum temperatures for the south of Buenos Aires province
title_fullStr Predictive models of minimum temperatures for the south of Buenos Aires province
title_full_unstemmed Predictive models of minimum temperatures for the south of Buenos Aires province
title_sort Predictive models of minimum temperatures for the south of Buenos Aires province
dc.creator.none.fl_str_mv Hernandez, Gabriela Lorena
Muller, Gabriela Viviana
Villacampa, Yolanda
Navarro González, Francisco José
Aragonés, Luis
author Hernandez, Gabriela Lorena
author_facet Hernandez, Gabriela Lorena
Muller, Gabriela Viviana
Villacampa, Yolanda
Navarro González, Francisco José
Aragonés, Luis
author_role author
author2 Muller, Gabriela Viviana
Villacampa, Yolanda
Navarro González, Francisco José
Aragonés, Luis
author2_role author
author
author
author
dc.subject.none.fl_str_mv AGRICULTURE
CROP TEMPERATURE
FINITE ELEMENTS
PREDICTIVE MODELS
topic AGRICULTURE
CROP TEMPERATURE
FINITE ELEMENTS
PREDICTIVE MODELS
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.5
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Depending on the time of development of a crop temperature below 0 °C can cause damage to the plant, altering its development and subsequent yield. Since frosts are identified from the minimum air temperature, the objective of this research paper is to generate forecast -(predictive) models at 1, 3 and 5 days of the minimum daily temperature (Tmin) for Bahía Blanca city. Non-linear numerical models are generated using artificial neural networks and geometric models of finite elements. Six independent variables are used: temperature and dew point temperature at meteorological shelter level, relative humidity, cloudiness observed above the station, wind speed and direction measured at 10 m altitude. Data have been obtained between May and September from 1956 to 2015. Once the available data had been analyzed, this period was reduced to 2007–2015. For the selection of the most suitable model, the correlation coefficient of Pearson (R), the determination coefficient (R2) and the Mean Absolute Error (MAE) are evaluated. The results of the study determine that the geometric model of finite elements with 4 variables, over 9 years (2007–2015) and separated by the season of the year is the one that presents better adjustment in the forecast of Tmin with up to 5 days of anticipation.
Fil: Hernandez, Gabriela Lorena. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Agronomía; Argentina
Fil: Muller, Gabriela Viviana. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Centro de Estudios de Variabilidad y Cambio Climático; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina
Fil: Villacampa, Yolanda. Universidad de Alicante; España
Fil: Navarro González, Francisco José. Universidad de Alicante; España
Fil: Aragonés, Luis. Universidad de Alicante; España
description Depending on the time of development of a crop temperature below 0 °C can cause damage to the plant, altering its development and subsequent yield. Since frosts are identified from the minimum air temperature, the objective of this research paper is to generate forecast -(predictive) models at 1, 3 and 5 days of the minimum daily temperature (Tmin) for Bahía Blanca city. Non-linear numerical models are generated using artificial neural networks and geometric models of finite elements. Six independent variables are used: temperature and dew point temperature at meteorological shelter level, relative humidity, cloudiness observed above the station, wind speed and direction measured at 10 m altitude. Data have been obtained between May and September from 1956 to 2015. Once the available data had been analyzed, this period was reduced to 2007–2015. For the selection of the most suitable model, the correlation coefficient of Pearson (R), the determination coefficient (R2) and the Mean Absolute Error (MAE) are evaluated. The results of the study determine that the geometric model of finite elements with 4 variables, over 9 years (2007–2015) and separated by the season of the year is the one that presents better adjustment in the forecast of Tmin with up to 5 days of anticipation.
publishDate 2020
dc.date.none.fl_str_mv 2020-01
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/149607
Hernandez, Gabriela Lorena; Muller, Gabriela Viviana; Villacampa, Yolanda; Navarro González, Francisco José; Aragonés, Luis; Predictive models of minimum temperatures for the south of Buenos Aires province; Elsevier; Science of the Total Environment; 699; 1-2020; 1-42
0048-9697
CONICET Digital
CONICET
url http://hdl.handle.net/11336/149607
identifier_str_mv Hernandez, Gabriela Lorena; Muller, Gabriela Viviana; Villacampa, Yolanda; Navarro González, Francisco José; Aragonés, Luis; Predictive models of minimum temperatures for the south of Buenos Aires province; Elsevier; Science of the Total Environment; 699; 1-2020; 1-42
0048-9697
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/abs/pii/S0048969719342639
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.scitotenv.2019.134280
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1844614070715547648
score 13.070432

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