Prediction of Road Transport of Wood in Uruguay: Approach with Machine Learning
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.3390/f13101737 http://hdl.handle.net/11449/246179 |
Resumo: | Among the activities that burden capital in the supply chain of forest-based industries, the activity of road transport of wood deserves to be highlighted. Machine learning techniques are applied the knowledge extracted from real data, and support strategies that aim to maximize the resources destined for it. Based on variables inherent to the wood transport activity, we verified whether machine learning models can act as predictors of the volume of wood to be transported and support strategic decision-making. The database came from companies in the pulp and paper segments, which totaled 26,761 data instances. After the data wrangling process, machine learning algorithms were used to build models, which were optimized from the hyperparameter adjustment and selected to compose the blended learning hierarchy. In addition to belonging to different methodological basis, a CatBoost Regressor, Decision Tree Regressor, and K Neighbors Regressor were selected mainly for providing minimal values to errors metrics and maximal values to determination coefficient. The learning by stack stands out, with a coefficient of determination of 0.70 and an average absolute percentage error of 6% in the estimation of the volume of wood to be transported. Based on variables inherent to the wood transport process, we verified that machine learning models can act in the prediction of the volume of wood to be transported and support strategic decision-making. |
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Repositório Institucional da UNESP |
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Prediction of Road Transport of Wood in Uruguay: Approach with Machine Learningdecision makingEucalyptusforest planningmachine learningplanted forestsprediction modelAmong the activities that burden capital in the supply chain of forest-based industries, the activity of road transport of wood deserves to be highlighted. Machine learning techniques are applied the knowledge extracted from real data, and support strategies that aim to maximize the resources destined for it. Based on variables inherent to the wood transport activity, we verified whether machine learning models can act as predictors of the volume of wood to be transported and support strategic decision-making. The database came from companies in the pulp and paper segments, which totaled 26,761 data instances. After the data wrangling process, machine learning algorithms were used to build models, which were optimized from the hyperparameter adjustment and selected to compose the blended learning hierarchy. In addition to belonging to different methodological basis, a CatBoost Regressor, Decision Tree Regressor, and K Neighbors Regressor were selected mainly for providing minimal values to errors metrics and maximal values to determination coefficient. The learning by stack stands out, with a coefficient of determination of 0.70 and an average absolute percentage error of 6% in the estimation of the volume of wood to be transported. Based on variables inherent to the wood transport process, we verified that machine learning models can act in the prediction of the volume of wood to be transported and support strategic decision-making.Department of Forest Science Soils and Environment School of Agriculture São Paulo State University (UNESP)Department of Forest Science Soils and Environment School of Agriculture São Paulo State University (UNESP)Universidade Estadual Paulista (UNESP)Almeida, Rodrigo Oliveira [UNESP]Munis, Rafaele Almeida [UNESP]Camargo, Diego Aparecido [UNESP]da Silva, Thamires [UNESP]Sasso Júnior, Valier Augusto [UNESP]Simões, Danilo [UNESP]2023-07-29T12:33:53Z2023-07-29T12:33:53Z2022-10-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.3390/f13101737Forests, v. 13, n. 10, 2022.1999-4907http://hdl.handle.net/11449/24617910.3390/f131017372-s2.0-85140794003Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengForestsinfo:eu-repo/semantics/openAccess2023-07-29T12:33:53Zoai:repositorio.unesp.br:11449/246179Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T16:42:17.116304Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Prediction of Road Transport of Wood in Uruguay: Approach with Machine Learning |
title |
Prediction of Road Transport of Wood in Uruguay: Approach with Machine Learning |
spellingShingle |
Prediction of Road Transport of Wood in Uruguay: Approach with Machine Learning Almeida, Rodrigo Oliveira [UNESP] decision making Eucalyptus forest planning machine learning planted forests prediction model |
title_short |
Prediction of Road Transport of Wood in Uruguay: Approach with Machine Learning |
title_full |
Prediction of Road Transport of Wood in Uruguay: Approach with Machine Learning |
title_fullStr |
Prediction of Road Transport of Wood in Uruguay: Approach with Machine Learning |
title_full_unstemmed |
Prediction of Road Transport of Wood in Uruguay: Approach with Machine Learning |
title_sort |
Prediction of Road Transport of Wood in Uruguay: Approach with Machine Learning |
author |
Almeida, Rodrigo Oliveira [UNESP] |
author_facet |
Almeida, Rodrigo Oliveira [UNESP] Munis, Rafaele Almeida [UNESP] Camargo, Diego Aparecido [UNESP] da Silva, Thamires [UNESP] Sasso Júnior, Valier Augusto [UNESP] Simões, Danilo [UNESP] |
author_role |
author |
author2 |
Munis, Rafaele Almeida [UNESP] Camargo, Diego Aparecido [UNESP] da Silva, Thamires [UNESP] Sasso Júnior, Valier Augusto [UNESP] Simões, Danilo [UNESP] |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (UNESP) |
dc.contributor.author.fl_str_mv |
Almeida, Rodrigo Oliveira [UNESP] Munis, Rafaele Almeida [UNESP] Camargo, Diego Aparecido [UNESP] da Silva, Thamires [UNESP] Sasso Júnior, Valier Augusto [UNESP] Simões, Danilo [UNESP] |
dc.subject.por.fl_str_mv |
decision making Eucalyptus forest planning machine learning planted forests prediction model |
topic |
decision making Eucalyptus forest planning machine learning planted forests prediction model |
description |
Among the activities that burden capital in the supply chain of forest-based industries, the activity of road transport of wood deserves to be highlighted. Machine learning techniques are applied the knowledge extracted from real data, and support strategies that aim to maximize the resources destined for it. Based on variables inherent to the wood transport activity, we verified whether machine learning models can act as predictors of the volume of wood to be transported and support strategic decision-making. The database came from companies in the pulp and paper segments, which totaled 26,761 data instances. After the data wrangling process, machine learning algorithms were used to build models, which were optimized from the hyperparameter adjustment and selected to compose the blended learning hierarchy. In addition to belonging to different methodological basis, a CatBoost Regressor, Decision Tree Regressor, and K Neighbors Regressor were selected mainly for providing minimal values to errors metrics and maximal values to determination coefficient. The learning by stack stands out, with a coefficient of determination of 0.70 and an average absolute percentage error of 6% in the estimation of the volume of wood to be transported. Based on variables inherent to the wood transport process, we verified that machine learning models can act in the prediction of the volume of wood to be transported and support strategic decision-making. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-10-01 2023-07-29T12:33:53Z 2023-07-29T12:33:53Z |
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/10.3390/f13101737 Forests, v. 13, n. 10, 2022. 1999-4907 http://hdl.handle.net/11449/246179 10.3390/f13101737 2-s2.0-85140794003 |
url |
http://dx.doi.org/10.3390/f13101737 http://hdl.handle.net/11449/246179 |
identifier_str_mv |
Forests, v. 13, n. 10, 2022. 1999-4907 10.3390/f13101737 2-s2.0-85140794003 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Forests |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
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_ |
1808128689011949568 |