Modelos estatísticos e técnicas de inteligência artificial para estimativa do volume de clones de Eucalyptus spp. com adição de variáveis climáticas
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Tipo de documento: | Tese |
Idioma: | por |
Título da fonte: | Biblioteca Digital de Teses e Dissertações da UFRPE |
Texto Completo: | http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/8753 |
Resumo: | The Gypsum Pole of Araripe is the main plaster producer in Brazil. To dehydrate the gypsum mineral that becomes plaster, the main energy source is firewood from the Caatinga, which has been exploited generally in an ilegal manner. Nowadays the effects of deforestation in the Caatinga combined with climate change have made the supply of energy resources scarce. As a way of mitigating the effect of deforestation and reducing the suppression of Caatinga vegetation, experiments with fast-growing forests were implanted in the region. In order to provide a more adequate development of the management of these forests, it is necessary to adjust local statistical models that are more efficient and that take into account changes in climate variables. Thus, the objective of this research was to adjust nonlinear models and machine learning algorithms and to evaluate whether the inclusion of climatic variables generates equations with greater precision and generalization power in the estimation of production and volumetric growth of textit Eucalyptus in a semi-arid climate region in Northeast Brazil. The data were obtained through a research carried out at the IPA Experimental Station, municipality of Araripina - PE, with three clones of Eucalyptus spp. (C39, C41, C11) planted in spacing 3 mx 3 m, 4 m x 2 m, 3 m x 2 m, 2 m x 2 m and 2 m, x 1 m, in a multivariate experiment of repeated measures, implemented in 2010. A height and diameter measurements were taken every six months. At the age of 96 months, the volume was determined by the Smalian method. The rainy season database (2002 to 2009) was obtained from an experiment developed in the same region with the same silvicultural treatments. Nonlinear models and artificial intelligence algorithms were used for volume production, growth and prognosis. The adjustments were evaluated based on the corrected Schlaegel adjustment index (IA c), Furnival index (IF), standard error of percentage estimate (Sxy %), root mean square error (RMSE), mean absolute error (MAE) and graphical analysis of the residues. Volumetric production differed among clones and between different levels of density. Symmetric models and generalized nonlinear models produced equations with high AIc values and low values of S xy % with residuals meeting the assumptions of the regression. The artificial intelligence techniques have efficiently adapted the estimate of production and growth in volume, generating statistics of good quality. The inclusion of climatic variables in the growth and prognosis models improves the estimates. |
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SILVA, José Antônio Aleixo daFINGER, César Augusto GuimarãesFERREIRA, Rinaldo Luiz CaracioloGUERA, Ouorou Ganni MarielGADELHA, Fernando Henrique de Limahttp://lattes.cnpq.br/9515094070486468SILVA, José Wesley Lima2022-12-07T16:05:47Z2020-02-28SILVA, José Wesley Lima. Modelos estatísticos e técnicas de inteligência artificial para estimativa do volume de clones de Eucalyptus spp. com adição de variáveis climáticas. 2020. 153 f. Tese (Programa de Pós-Graduação em Biometria e Estatística Aplicada) - Universidade Federal Rural de Pernambuco, Recife.http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/8753The Gypsum Pole of Araripe is the main plaster producer in Brazil. To dehydrate the gypsum mineral that becomes plaster, the main energy source is firewood from the Caatinga, which has been exploited generally in an ilegal manner. Nowadays the effects of deforestation in the Caatinga combined with climate change have made the supply of energy resources scarce. As a way of mitigating the effect of deforestation and reducing the suppression of Caatinga vegetation, experiments with fast-growing forests were implanted in the region. In order to provide a more adequate development of the management of these forests, it is necessary to adjust local statistical models that are more efficient and that take into account changes in climate variables. Thus, the objective of this research was to adjust nonlinear models and machine learning algorithms and to evaluate whether the inclusion of climatic variables generates equations with greater precision and generalization power in the estimation of production and volumetric growth of textit Eucalyptus in a semi-arid climate region in Northeast Brazil. The data were obtained through a research carried out at the IPA Experimental Station, municipality of Araripina - PE, with three clones of Eucalyptus spp. (C39, C41, C11) planted in spacing 3 mx 3 m, 4 m x 2 m, 3 m x 2 m, 2 m x 2 m and 2 m, x 1 m, in a multivariate experiment of repeated measures, implemented in 2010. A height and diameter measurements were taken every six months. At the age of 96 months, the volume was determined by the Smalian method. The rainy season database (2002 to 2009) was obtained from an experiment developed in the same region with the same silvicultural treatments. Nonlinear models and artificial intelligence algorithms were used for volume production, growth and prognosis. The adjustments were evaluated based on the corrected Schlaegel adjustment index (IA c), Furnival index (IF), standard error of percentage estimate (Sxy %), root mean square error (RMSE), mean absolute error (MAE) and graphical analysis of the residues. Volumetric production differed among clones and between different levels of density. Symmetric models and generalized nonlinear models produced equations with high AIc values and low values of S xy % with residuals meeting the assumptions of the regression. The artificial intelligence techniques have efficiently adapted the estimate of production and growth in volume, generating statistics of good quality. The inclusion of climatic variables in the growth and prognosis models improves the estimates.O Polo Gesseiro do Araripe é o principal produtor de gesso do Brasil. Para o processamento do minério gipsita a principal fonte energética é a lenha proveniente da Caatinga que tem sido explorada geralmente de forma ilegal. Nos dias atuais os efeitos do desmatamento da Caatinga combinado com as mudanças climáticas tornou a oferta dos recursos energéticos escassa. Como forma de mitigar o efeito do desmatamento e diminuir a supressão da vegetação Caatinga, experimentos com florestas de rápido crescimento foram implantadas na região. Para proporcionar um desenvolvimento mais adequado do manejo dessas florestas se faz necessário o ajuste de modelos estatísticos locais mais eficientes e que levem em consideração a mudança nas variáveis do clima. Desta forma, objetivou-se com esta pesquisa ajustar modelos não lineares e algoritmos da aprendizagem de máquinas e avaliar se a inclusão de variáveis climáticas gera equações com maior precisão e poder de generalização na estimativa da produção e do crescimento volumétrico de clones de Eucalyptus em região de clima semiárido do Nordeste brasileiro. Os dados foram obtidos por meio de uma pesquisa realizada na Estação Experimental do IPA, município de Araripina - PE, com três clones de Eucalyptus spp. (C39, C41, C11) plantados nos espaçamentos 3 m x 3 m, 4 m x 2 m, 3 m x 2 m, 2 m x 2 m e 2 m, x 1 m, em experimento multivariado de medidas repetidas, implantado no ano de 2010. A cada seis meses foram realizadas medições de altura e diâmetro à altura do peito. Na idade de 96 meses o experimento foi cortado e o volume de madeira foi cubado rigorosamente pelo método de Smalian. A base de dados do período chuvoso (2002 à 2009) foi obtida de um experimento desenvolvido na mesma região com os mesmos tratos silviculturais. Foram empregados modelos não lineares e algoritmos da inteligência artificial para produção, crescimento e prognose do volume. Os ajustes foram avaliados com base no índice de ajuste de Schlaegel corrigido (IAc), índice de Furnival (IF), erro padrão de estimativa em porcentagem (Sxy%), raiz do erro quadrático médio (REQM), erro absoluto médio (EAM) e análise gráfica dos resíduos. A produção volumétrica se diferenciou entre clones e entre os diferentes níveis de adensamento. Os modelos simétricos e os modelos não lineares generalizados produziram equações com altos valores de IAc e baixos valores de Sxy% e com resíduos atendendo aos pressupostos da regressão. As técnicas de inteligência artificial se adequaram com eficiência a estimativa da produção e do crescimento em volume gerando estatísticas de boa qualidade. A inclusão das variáveis climáticas nos modelos de crescimento e prognose melhoram as estimativas.Submitted by (lucia.rodrigues@ufrpe.br) on 2022-12-07T16:05:47Z No. of bitstreams: 1 Jose Wesley Lima Silva.pdf: 7907251 bytes, checksum: 38194fbb11c7289e62e88a5b36503471 (MD5)Made available in DSpace on 2022-12-07T16:05:47Z (GMT). No. of bitstreams: 1 Jose Wesley Lima Silva.pdf: 7907251 bytes, checksum: 38194fbb11c7289e62e88a5b36503471 (MD5) Previous issue date: 2020-02-28Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPESapplication/pdfporUniversidade Federal Rural de PernambucoPrograma de Pós-Graduação em Biometria e Estatística AplicadaUFRPEBrasilDepartamento de Estatística e InformáticaMudança climáticaModelo simétricoModelo não linear generalizadoRede neural artificialInteligência artificialAprendizagem de máquinaPolo gesseiroCIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICAModelos estatísticos e técnicas de inteligência artificial para estimativa do volume de clones de Eucalyptus spp. com adição de variáveis climáticasinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesis768382242446187918600600600600-6774555140396120501-58364078281851435172075167498588264571info:eu-repo/semantics/openAccessreponame:Biblioteca Digital de Teses e Dissertações da UFRPEinstname:Universidade Federal Rural de Pernambuco (UFRPE)instacron:UFRPEORIGINALJose Wesley Lima Silva.pdfJose Wesley Lima Silva.pdfapplication/pdf7907251http://www.tede2.ufrpe.br:8080/tede2/bitstream/tede2/8753/2/Jose+Wesley+Lima+Silva.pdf38194fbb11c7289e62e88a5b36503471MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-82165http://www.tede2.ufrpe.br:8080/tede2/bitstream/tede2/8753/1/license.txtbd3efa91386c1718a7f26a329fdcb468MD51tede2/87532024-02-20 11:31:26.552oai:tede2: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Biblioteca Digital de Teses e Dissertaçõeshttp://www.tede2.ufrpe.br:8080/tede/PUBhttp://www.tede2.ufrpe.br:8080/oai/requestbdtd@ufrpe.br ||bdtd@ufrpe.bropendoar:2024-05-28T12:37:23.126340Biblioteca Digital de Teses e Dissertações da UFRPE - Universidade Federal Rural de Pernambuco (UFRPE)false |
dc.title.por.fl_str_mv |
Modelos estatísticos e técnicas de inteligência artificial para estimativa do volume de clones de Eucalyptus spp. com adição de variáveis climáticas |
title |
Modelos estatísticos e técnicas de inteligência artificial para estimativa do volume de clones de Eucalyptus spp. com adição de variáveis climáticas |
spellingShingle |
Modelos estatísticos e técnicas de inteligência artificial para estimativa do volume de clones de Eucalyptus spp. com adição de variáveis climáticas SILVA, José Wesley Lima Mudança climática Modelo simétrico Modelo não linear generalizado Rede neural artificial Inteligência artificial Aprendizagem de máquina Polo gesseiro CIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICA |
title_short |
Modelos estatísticos e técnicas de inteligência artificial para estimativa do volume de clones de Eucalyptus spp. com adição de variáveis climáticas |
title_full |
Modelos estatísticos e técnicas de inteligência artificial para estimativa do volume de clones de Eucalyptus spp. com adição de variáveis climáticas |
title_fullStr |
Modelos estatísticos e técnicas de inteligência artificial para estimativa do volume de clones de Eucalyptus spp. com adição de variáveis climáticas |
title_full_unstemmed |
Modelos estatísticos e técnicas de inteligência artificial para estimativa do volume de clones de Eucalyptus spp. com adição de variáveis climáticas |
title_sort |
Modelos estatísticos e técnicas de inteligência artificial para estimativa do volume de clones de Eucalyptus spp. com adição de variáveis climáticas |
author |
SILVA, José Wesley Lima |
author_facet |
SILVA, José Wesley Lima |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
SILVA, José Antônio Aleixo da |
dc.contributor.referee1.fl_str_mv |
FINGER, César Augusto Guimarães |
dc.contributor.referee2.fl_str_mv |
FERREIRA, Rinaldo Luiz Caraciolo |
dc.contributor.referee3.fl_str_mv |
GUERA, Ouorou Ganni Mariel |
dc.contributor.referee4.fl_str_mv |
GADELHA, Fernando Henrique de Lima |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/9515094070486468 |
dc.contributor.author.fl_str_mv |
SILVA, José Wesley Lima |
contributor_str_mv |
SILVA, José Antônio Aleixo da FINGER, César Augusto Guimarães FERREIRA, Rinaldo Luiz Caraciolo GUERA, Ouorou Ganni Mariel GADELHA, Fernando Henrique de Lima |
dc.subject.por.fl_str_mv |
Mudança climática Modelo simétrico Modelo não linear generalizado Rede neural artificial Inteligência artificial Aprendizagem de máquina Polo gesseiro |
topic |
Mudança climática Modelo simétrico Modelo não linear generalizado Rede neural artificial Inteligência artificial Aprendizagem de máquina Polo gesseiro CIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICA |
dc.subject.cnpq.fl_str_mv |
CIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICA |
description |
The Gypsum Pole of Araripe is the main plaster producer in Brazil. To dehydrate the gypsum mineral that becomes plaster, the main energy source is firewood from the Caatinga, which has been exploited generally in an ilegal manner. Nowadays the effects of deforestation in the Caatinga combined with climate change have made the supply of energy resources scarce. As a way of mitigating the effect of deforestation and reducing the suppression of Caatinga vegetation, experiments with fast-growing forests were implanted in the region. In order to provide a more adequate development of the management of these forests, it is necessary to adjust local statistical models that are more efficient and that take into account changes in climate variables. Thus, the objective of this research was to adjust nonlinear models and machine learning algorithms and to evaluate whether the inclusion of climatic variables generates equations with greater precision and generalization power in the estimation of production and volumetric growth of textit Eucalyptus in a semi-arid climate region in Northeast Brazil. The data were obtained through a research carried out at the IPA Experimental Station, municipality of Araripina - PE, with three clones of Eucalyptus spp. (C39, C41, C11) planted in spacing 3 mx 3 m, 4 m x 2 m, 3 m x 2 m, 2 m x 2 m and 2 m, x 1 m, in a multivariate experiment of repeated measures, implemented in 2010. A height and diameter measurements were taken every six months. At the age of 96 months, the volume was determined by the Smalian method. The rainy season database (2002 to 2009) was obtained from an experiment developed in the same region with the same silvicultural treatments. Nonlinear models and artificial intelligence algorithms were used for volume production, growth and prognosis. The adjustments were evaluated based on the corrected Schlaegel adjustment index (IA c), Furnival index (IF), standard error of percentage estimate (Sxy %), root mean square error (RMSE), mean absolute error (MAE) and graphical analysis of the residues. Volumetric production differed among clones and between different levels of density. Symmetric models and generalized nonlinear models produced equations with high AIc values and low values of S xy % with residuals meeting the assumptions of the regression. The artificial intelligence techniques have efficiently adapted the estimate of production and growth in volume, generating statistics of good quality. The inclusion of climatic variables in the growth and prognosis models improves the estimates. |
publishDate |
2020 |
dc.date.issued.fl_str_mv |
2020-02-28 |
dc.date.accessioned.fl_str_mv |
2022-12-07T16:05:47Z |
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info:eu-repo/semantics/publishedVersion |
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info:eu-repo/semantics/doctoralThesis |
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doctoralThesis |
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publishedVersion |
dc.identifier.citation.fl_str_mv |
SILVA, José Wesley Lima. Modelos estatísticos e técnicas de inteligência artificial para estimativa do volume de clones de Eucalyptus spp. com adição de variáveis climáticas. 2020. 153 f. Tese (Programa de Pós-Graduação em Biometria e Estatística Aplicada) - Universidade Federal Rural de Pernambuco, Recife. |
dc.identifier.uri.fl_str_mv |
http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/8753 |
identifier_str_mv |
SILVA, José Wesley Lima. Modelos estatísticos e técnicas de inteligência artificial para estimativa do volume de clones de Eucalyptus spp. com adição de variáveis climáticas. 2020. 153 f. Tese (Programa de Pós-Graduação em Biometria e Estatística Aplicada) - Universidade Federal Rural de Pernambuco, Recife. |
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http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/8753 |
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Universidade Federal Rural de Pernambuco |
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Universidade Federal Rural de Pernambuco |
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