Enthalpy-based decision trees for comfort assessment of light layers in a tropical environment
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Research, Society and Development |
Texto Completo: | https://rsdjournal.org/index.php/rsd/article/view/13354 |
Resumo: | With the advancement of technology, it was possible to store a large amount of data at a lower price, making it possible to investigate the climatic factors that affect animal production. The western region of the state of São Paulo is a region prone to extremes of temperature, which is a worrying factor for egg producers. This study aimed to find out if it is possible to generate decision trees from enthalpy data using Data Mining, and if these trees are suitable to be inserted in a weather forecast system. For this study, a database of bioclimatic variables from three commercial layer aviaries located in the city of Bastos-SP, Brazil, in the year 2013 was used. After organizing and classifying the data in a spreadsheet, and processed by Weka software with the J48 algorithm (C4.5) for data mining, this technique applied to this database allowed the generation of decision trees with approximately 98% and 0.96 for Kappa index, respectively. Thus, the decision trees generated in this study are accurate enough to be used in a future warning system against climatic adversities for commercial layers in tropical climates. |
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Enthalpy-based decision trees for comfort assessment of light layers in a tropical environmentÁrboles de decisión basados en la entalpía para la evaluación del confort de las ponedoras en clima tropicalÁrvores de decisão baseadas em entalpia para avaliação de conforto para poedeiras leves em clima tropical Climate extremesData miningLayer poultry.Extremos ClimáticosMinería de datosAves de Corral ponedoras.Extremos climáticosMineração de dadosAvicultura de postura.With the advancement of technology, it was possible to store a large amount of data at a lower price, making it possible to investigate the climatic factors that affect animal production. The western region of the state of São Paulo is a region prone to extremes of temperature, which is a worrying factor for egg producers. This study aimed to find out if it is possible to generate decision trees from enthalpy data using Data Mining, and if these trees are suitable to be inserted in a weather forecast system. For this study, a database of bioclimatic variables from three commercial layer aviaries located in the city of Bastos-SP, Brazil, in the year 2013 was used. After organizing and classifying the data in a spreadsheet, and processed by Weka software with the J48 algorithm (C4.5) for data mining, this technique applied to this database allowed the generation of decision trees with approximately 98% and 0.96 for Kappa index, respectively. Thus, the decision trees generated in this study are accurate enough to be used in a future warning system against climatic adversities for commercial layers in tropical climates.Con el avance de la tecnología fue posible almacenar una gran cantidad de datos con un precio menor, siendo posible investigar los factores climáticos que afectan a la producción animal. La región occidental del estado de São Paulo es una región propensa a la ocurrencia de temperaturas extremas, lo que constituye un factor preocupante para los productores de huevos. En este estudio, el objetivo era saber si es posible generar árboles de decisión a partir de datos de entalpía utilizando la Minería de Datos, y si estos árboles serán adecuados para ser insertados en un sistema de alerta meteorológica. Para este estudio, se utilizó una base de datos de variables bioclimáticas de tres granjas comerciales de ponedoras ubicadas en la ciudad de Bastos-SP, Brasil, en 2013. Después de organizar y clasificar los datos en una hoja de cálculo electrónica, y procesados por el software Weka con el algoritmo J48 (C4.5) para la minería de datos. Esta técnica aplicada a esta base de datos permitió generar árboles de decisión con aproximadamente un 98% de respuestas correctas y un índice Kappa de 0,96 respectivamente. Por lo tanto, los árboles de decisión generados en este estudio son lo suficientemente precisos como para ser utilizados en el futuro en un sistema de alerta contra adversidades climáticas para capas comerciales en climas tropicales.Com o avanço da tecnologia foi possível armazenar uma grande quantidade de dados com um menor preço, sendo possível investigar os fatores climáticos que afetam a produção animal. A região Oeste do Estado de São Paulo é uma região propensa à ocorrência de extremos de temperatura, sendo um fator preocupante para os produtores de ovos. Neste estudo objetivou-se saber se é possível gerar árvores de decisão a partir de dados de entalpia utilizando a Mineração de Dados, e se estas árvores estarão aptas a serem inseridas em um sistema de alerta para intempéries climáticas. Para este estudo foi utilizado um banco de dados de variáveis bioclimáticas provenientes de três aviários de poedeiras comerciais localizados na cidade de Bastos-SP, Brasil, no ano de 2013. Após a organização e classificação dos dados em planilha eletrônica, e processados pelo software Weka com o algoritmo J48 (C4.5) para mineração de dados, a técnica aplicada a este banco de dados possibilitou a geração de árvores de decisão com aproximadamente 98% de acertos e índice Kappa de 0,96 respectivamente. Sendo assim, as árvores de decisão geradas neste estudo são precisas o suficiente, para que sejam utilizadas futuramente em um sistema de alerta contra adversidades climáticas para poedeiras comerciais em clima tropical.Research, Society and Development2021-03-20info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://rsdjournal.org/index.php/rsd/article/view/1335410.33448/rsd-v10i3.13354Research, Society and Development; Vol. 10 No. 3; e45310313354Research, Society and Development; Vol. 10 Núm. 3; e45310313354Research, Society and Development; v. 10 n. 3; e453103133542525-3409reponame:Research, Society and Developmentinstname:Universidade Federal de Itajubá (UNIFEI)instacron:UNIFEIporhttps://rsdjournal.org/index.php/rsd/article/view/13354/12135Copyright (c) 2021 Érik dos Santos Harada; Maria Elena Silva Montanhani ; Leda Gobbo de Freitas Bueno; Mario Mollo Neto; Silvia Regina Lucas de Souza; Ricardo da Fonsecahttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessHarada, Érik dos Santos Montanhani , Maria Elena Silva Bueno, Leda Gobbo de Freitas Mollo Neto, MarioSouza, Silvia Regina Lucas de Fonseca, Ricardo da 2021-03-28T12:03:35Zoai:ojs.pkp.sfu.ca:article/13354Revistahttps://rsdjournal.org/index.php/rsd/indexPUBhttps://rsdjournal.org/index.php/rsd/oairsd.articles@gmail.com2525-34092525-3409opendoar:2024-01-17T09:34:42.996078Research, Society and Development - Universidade Federal de Itajubá (UNIFEI)false |
dc.title.none.fl_str_mv |
Enthalpy-based decision trees for comfort assessment of light layers in a tropical environment Árboles de decisión basados en la entalpía para la evaluación del confort de las ponedoras en clima tropical Árvores de decisão baseadas em entalpia para avaliação de conforto para poedeiras leves em clima tropical |
title |
Enthalpy-based decision trees for comfort assessment of light layers in a tropical environment |
spellingShingle |
Enthalpy-based decision trees for comfort assessment of light layers in a tropical environment Harada, Érik dos Santos Climate extremes Data mining Layer poultry. Extremos Climáticos Minería de datos Aves de Corral ponedoras. Extremos climáticos Mineração de dados Avicultura de postura. |
title_short |
Enthalpy-based decision trees for comfort assessment of light layers in a tropical environment |
title_full |
Enthalpy-based decision trees for comfort assessment of light layers in a tropical environment |
title_fullStr |
Enthalpy-based decision trees for comfort assessment of light layers in a tropical environment |
title_full_unstemmed |
Enthalpy-based decision trees for comfort assessment of light layers in a tropical environment |
title_sort |
Enthalpy-based decision trees for comfort assessment of light layers in a tropical environment |
author |
Harada, Érik dos Santos |
author_facet |
Harada, Érik dos Santos Montanhani , Maria Elena Silva Bueno, Leda Gobbo de Freitas Mollo Neto, Mario Souza, Silvia Regina Lucas de Fonseca, Ricardo da |
author_role |
author |
author2 |
Montanhani , Maria Elena Silva Bueno, Leda Gobbo de Freitas Mollo Neto, Mario Souza, Silvia Regina Lucas de Fonseca, Ricardo da |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
Harada, Érik dos Santos Montanhani , Maria Elena Silva Bueno, Leda Gobbo de Freitas Mollo Neto, Mario Souza, Silvia Regina Lucas de Fonseca, Ricardo da |
dc.subject.por.fl_str_mv |
Climate extremes Data mining Layer poultry. Extremos Climáticos Minería de datos Aves de Corral ponedoras. Extremos climáticos Mineração de dados Avicultura de postura. |
topic |
Climate extremes Data mining Layer poultry. Extremos Climáticos Minería de datos Aves de Corral ponedoras. Extremos climáticos Mineração de dados Avicultura de postura. |
description |
With the advancement of technology, it was possible to store a large amount of data at a lower price, making it possible to investigate the climatic factors that affect animal production. The western region of the state of São Paulo is a region prone to extremes of temperature, which is a worrying factor for egg producers. This study aimed to find out if it is possible to generate decision trees from enthalpy data using Data Mining, and if these trees are suitable to be inserted in a weather forecast system. For this study, a database of bioclimatic variables from three commercial layer aviaries located in the city of Bastos-SP, Brazil, in the year 2013 was used. After organizing and classifying the data in a spreadsheet, and processed by Weka software with the J48 algorithm (C4.5) for data mining, this technique applied to this database allowed the generation of decision trees with approximately 98% and 0.96 for Kappa index, respectively. Thus, the decision trees generated in this study are accurate enough to be used in a future warning system against climatic adversities for commercial layers in tropical climates. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-03-20 |
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 |
https://rsdjournal.org/index.php/rsd/article/view/13354 10.33448/rsd-v10i3.13354 |
url |
https://rsdjournal.org/index.php/rsd/article/view/13354 |
identifier_str_mv |
10.33448/rsd-v10i3.13354 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
https://rsdjournal.org/index.php/rsd/article/view/13354/12135 |
dc.rights.driver.fl_str_mv |
https://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
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 |
Research, Society and Development |
publisher.none.fl_str_mv |
Research, Society and Development |
dc.source.none.fl_str_mv |
Research, Society and Development; Vol. 10 No. 3; e45310313354 Research, Society and Development; Vol. 10 Núm. 3; e45310313354 Research, Society and Development; v. 10 n. 3; e45310313354 2525-3409 reponame:Research, Society and Development instname:Universidade Federal de Itajubá (UNIFEI) instacron:UNIFEI |
instname_str |
Universidade Federal de Itajubá (UNIFEI) |
instacron_str |
UNIFEI |
institution |
UNIFEI |
reponame_str |
Research, Society and Development |
collection |
Research, Society and Development |
repository.name.fl_str_mv |
Research, Society and Development - Universidade Federal de Itajubá (UNIFEI) |
repository.mail.fl_str_mv |
rsd.articles@gmail.com |
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1797052817378115584 |