Estimativa dos parâmetros de funções de pedotransferência para os solos do Rio Grande do Sul
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Tipo de documento: | Tese |
Idioma: | por |
Título da fonte: | Biblioteca Digital de Teses e Dissertações do UFSM |
Texto Completo: | http://repositorio.ufsm.br/handle/1/17297 |
Resumo: | The soil water retention curve is an important information for the rational management of irrigation. Due to the difficulty in generating it through traditional methods, there is a need to create alternative methods, such as pedotransfer functions, that generate the curve indirectly, using data that can be acquired more easily and quickly. In this context, the present work aims to estimate the parameters of the Van Genuchten model through pedotransfer functions with physical-hydro data for soils of the State of Rio Grande do Sul using Multiple Linear Regression and Artificial Neural Networks. To estimate the parameters θr, α and n of the Van Genuchten equation, the levels of sand, silt, clay, soil density (ds), particle density (dp) and organic matter (Mo) were used as independent variables. Multiple linear regression analyzes were performed using the stepwise (Forward) procedure of the IBM SPSS Statistics 25 software, while the artificial neural networks were generated using the multiple layer perceptron function of the same software. The results obtained in the estimation of the parameters α, θr and n can be considered good with both estimation methodologies. In the multiple linear regression the values of the coefficient of determination were higher than 0.9 in most of the proposed models, and the root mean square error presented values lower than 0.008. Using artificial neural networks, the R² values were higher than 0.9 in all the proposed models, and the RMSE presented values lower than 0.0012. With this, we can affirm that the use of Multiple Linear Regression and Artificial Neural Networks was efficient in generating Pedotransfer Functions, for the data base used, estimating the parameters of the Van Genuchten equation with high predictive capacity of the proposed models. that the second methodology had higher values of R² and lower values of RMSE. |
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2019-07-04T14:43:11Z2019-07-04T14:43:11Z2019-04-29http://repositorio.ufsm.br/handle/1/17297The soil water retention curve is an important information for the rational management of irrigation. Due to the difficulty in generating it through traditional methods, there is a need to create alternative methods, such as pedotransfer functions, that generate the curve indirectly, using data that can be acquired more easily and quickly. In this context, the present work aims to estimate the parameters of the Van Genuchten model through pedotransfer functions with physical-hydro data for soils of the State of Rio Grande do Sul using Multiple Linear Regression and Artificial Neural Networks. To estimate the parameters θr, α and n of the Van Genuchten equation, the levels of sand, silt, clay, soil density (ds), particle density (dp) and organic matter (Mo) were used as independent variables. Multiple linear regression analyzes were performed using the stepwise (Forward) procedure of the IBM SPSS Statistics 25 software, while the artificial neural networks were generated using the multiple layer perceptron function of the same software. The results obtained in the estimation of the parameters α, θr and n can be considered good with both estimation methodologies. In the multiple linear regression the values of the coefficient of determination were higher than 0.9 in most of the proposed models, and the root mean square error presented values lower than 0.008. Using artificial neural networks, the R² values were higher than 0.9 in all the proposed models, and the RMSE presented values lower than 0.0012. With this, we can affirm that the use of Multiple Linear Regression and Artificial Neural Networks was efficient in generating Pedotransfer Functions, for the data base used, estimating the parameters of the Van Genuchten equation with high predictive capacity of the proposed models. that the second methodology had higher values of R² and lower values of RMSE.A curva de retenção de água no solo é uma informação importante para o manejo racional da irrigação. Devido a dificuldade na geração da mesma através de métodos tradicionais, existe a necessidade da criação de métodos alternativos, como as funções de pedotransferência, que geram a curva de forma indireta, utilizando dados que podem ser adquiridos de forma mais fácil e rápida. Neste contexto, o presente trabalho objetiva estimar os parâmetros do modelo de Van Genuchten através de funções de pedotransferência com dados físico-hídricos para solos do estado do Rio Grande do Sul utilizando Regressão Linear Múltipla e Redes Neurais Artificiais. Para estimar os parâmetros θr, α e n da equação de Van Genuchten, foram utilizados os teores de areia, silte, argila, densidade do solo (ds), densidade de partículas (dp) e Matéria Orgânica (Mo) como variáveis independentes. As análises de regressão linear múltipla foram realizadas utilizado o procedimento stepwise (Forward) do software IBM SPSS Statistics 25, enquanto que as redes neurais artificiais foram geradas utilizando a função perceptron múltipla camada do mesmo software. Os resultados obtidos na estimativa dos parâmetros α, θr e n podem ser considerados bons com ambas metodologias de estimativa. Na regressão linear múltipla os valores do coeficiente de determinação foram superiores a 0,9 na maioria dos modelos propostos, e a raíz do erro médio quadrado apresentou valores inferiores a 0,008. Usando redes neurais artificiais os valores de R² foram superiores a 0,9 em todos os modelos propostos, e a RMSE apresentou valores inferiores a 0,0012. Com isto, podemos afirmar que o uso de Regressão Linear Múltipla e das Redes Neurais Artificiais se mostrou eficiente em gerar Funções de Pedotransferência, para a base de dados utilizada, estimando os parâmetros da equação de Van Genuchten com alta capacidade preditiva dos modelos propostos, sendo que a segunda metodologia apresentou valores maiores de R² e menores de RMSE.porUniversidade Federal de Santa MariaCentro de Ciências RuraisPrograma de Pós-Graduação em Engenharia AgrícolaUFSMBrasilEngenharia AgrícolaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessRedes neurais artificiaisRegressão linear múltiplaVan GenuchtenCurva de retenção de água no soloSoil water retention curveArtificial neural networksMultiple linear regressionCNPQ::CIENCIAS AGRARIAS::ENGENHARIA AGRICOLAEstimativa dos parâmetros de funções de pedotransferência para os solos do Rio Grande do SulEstimation of the parameters of pedotransferance functions for soils of Rio Grande do Sulinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisPeiter, Marcia Xavierhttp://lattes.cnpq.br/4072803412132476Robaina, Adroaldo Diashttp://lattes.cnpq.br/8629241691140049Sebem, Elódiohttp://lattes.cnpq.br/7879588106056349Weber, Liane de Souzahttp://lattes.cnpq.br/2891799660226360Torres, Rogério Ricaldehttp://lattes.cnpq.br/5705962150564760http://lattes.cnpq.br/3780545950289957Kayser, Luiz Patric500300000008600aa9a3730-307d-44a2-8042-8c43bcf8e4b119d98c80-664d-4696-ba22-06a1636cbeb6b3a282ad-3e3e-42b1-b88c-5bc23251b462e8f79531-e15e-49b4-afda-71d0aa6ef45540423488-e9bc-4ade-8d19-46286076cba666385ca7-bb99-4c0f-883c-a806822bc3bcreponame:Biblioteca Digital de Teses e Dissertações do UFSMinstname:Universidade Federal de Santa Maria (UFSM)instacron:UFSMORIGINALTES_PPGEA_2019_KAYSER_LUIZ.pdfTES_PPGEA_2019_KAYSER_LUIZ.pdfTese de Doutoradoapplication/pdf1949274http://repositorio.ufsm.br/bitstream/1/17297/1/TES_PPGEA_2019_KAYSER_LUIZ.pdf9027ed2e56c4f7a34a109b2ea354b29aMD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; 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dc.title.por.fl_str_mv |
Estimativa dos parâmetros de funções de pedotransferência para os solos do Rio Grande do Sul |
dc.title.alternative.eng.fl_str_mv |
Estimation of the parameters of pedotransferance functions for soils of Rio Grande do Sul |
title |
Estimativa dos parâmetros de funções de pedotransferência para os solos do Rio Grande do Sul |
spellingShingle |
Estimativa dos parâmetros de funções de pedotransferência para os solos do Rio Grande do Sul Kayser, Luiz Patric Redes neurais artificiais Regressão linear múltipla Van Genuchten Curva de retenção de água no solo Soil water retention curve Artificial neural networks Multiple linear regression CNPQ::CIENCIAS AGRARIAS::ENGENHARIA AGRICOLA |
title_short |
Estimativa dos parâmetros de funções de pedotransferência para os solos do Rio Grande do Sul |
title_full |
Estimativa dos parâmetros de funções de pedotransferência para os solos do Rio Grande do Sul |
title_fullStr |
Estimativa dos parâmetros de funções de pedotransferência para os solos do Rio Grande do Sul |
title_full_unstemmed |
Estimativa dos parâmetros de funções de pedotransferência para os solos do Rio Grande do Sul |
title_sort |
Estimativa dos parâmetros de funções de pedotransferência para os solos do Rio Grande do Sul |
author |
Kayser, Luiz Patric |
author_facet |
Kayser, Luiz Patric |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Peiter, Marcia Xavier |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/4072803412132476 |
dc.contributor.advisor-co1.fl_str_mv |
Robaina, Adroaldo Dias |
dc.contributor.advisor-co1Lattes.fl_str_mv |
http://lattes.cnpq.br/8629241691140049 |
dc.contributor.referee1.fl_str_mv |
Sebem, Elódio |
dc.contributor.referee1Lattes.fl_str_mv |
http://lattes.cnpq.br/7879588106056349 |
dc.contributor.referee2.fl_str_mv |
Weber, Liane de Souza |
dc.contributor.referee2Lattes.fl_str_mv |
http://lattes.cnpq.br/2891799660226360 |
dc.contributor.referee3.fl_str_mv |
Torres, Rogério Ricalde |
dc.contributor.referee3Lattes.fl_str_mv |
http://lattes.cnpq.br/5705962150564760 |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/3780545950289957 |
dc.contributor.author.fl_str_mv |
Kayser, Luiz Patric |
contributor_str_mv |
Peiter, Marcia Xavier Robaina, Adroaldo Dias Sebem, Elódio Weber, Liane de Souza Torres, Rogério Ricalde |
dc.subject.por.fl_str_mv |
Redes neurais artificiais Regressão linear múltipla Van Genuchten Curva de retenção de água no solo Soil water retention curve |
topic |
Redes neurais artificiais Regressão linear múltipla Van Genuchten Curva de retenção de água no solo Soil water retention curve Artificial neural networks Multiple linear regression CNPQ::CIENCIAS AGRARIAS::ENGENHARIA AGRICOLA |
dc.subject.eng.fl_str_mv |
Artificial neural networks Multiple linear regression |
dc.subject.cnpq.fl_str_mv |
CNPQ::CIENCIAS AGRARIAS::ENGENHARIA AGRICOLA |
description |
The soil water retention curve is an important information for the rational management of irrigation. Due to the difficulty in generating it through traditional methods, there is a need to create alternative methods, such as pedotransfer functions, that generate the curve indirectly, using data that can be acquired more easily and quickly. In this context, the present work aims to estimate the parameters of the Van Genuchten model through pedotransfer functions with physical-hydro data for soils of the State of Rio Grande do Sul using Multiple Linear Regression and Artificial Neural Networks. To estimate the parameters θr, α and n of the Van Genuchten equation, the levels of sand, silt, clay, soil density (ds), particle density (dp) and organic matter (Mo) were used as independent variables. Multiple linear regression analyzes were performed using the stepwise (Forward) procedure of the IBM SPSS Statistics 25 software, while the artificial neural networks were generated using the multiple layer perceptron function of the same software. The results obtained in the estimation of the parameters α, θr and n can be considered good with both estimation methodologies. In the multiple linear regression the values of the coefficient of determination were higher than 0.9 in most of the proposed models, and the root mean square error presented values lower than 0.008. Using artificial neural networks, the R² values were higher than 0.9 in all the proposed models, and the RMSE presented values lower than 0.0012. With this, we can affirm that the use of Multiple Linear Regression and Artificial Neural Networks was efficient in generating Pedotransfer Functions, for the data base used, estimating the parameters of the Van Genuchten equation with high predictive capacity of the proposed models. that the second methodology had higher values of R² and lower values of RMSE. |
publishDate |
2019 |
dc.date.accessioned.fl_str_mv |
2019-07-04T14:43:11Z |
dc.date.available.fl_str_mv |
2019-07-04T14:43:11Z |
dc.date.issued.fl_str_mv |
2019-04-29 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/doctoralThesis |
format |
doctoralThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://repositorio.ufsm.br/handle/1/17297 |
url |
http://repositorio.ufsm.br/handle/1/17297 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.cnpq.fl_str_mv |
500300000008 |
dc.relation.confidence.fl_str_mv |
600 |
dc.relation.authority.fl_str_mv |
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dc.rights.driver.fl_str_mv |
Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Universidade Federal de Santa Maria Centro de Ciências Rurais |
dc.publisher.program.fl_str_mv |
Programa de Pós-Graduação em Engenharia Agrícola |
dc.publisher.initials.fl_str_mv |
UFSM |
dc.publisher.country.fl_str_mv |
Brasil |
dc.publisher.department.fl_str_mv |
Engenharia Agrícola |
publisher.none.fl_str_mv |
Universidade Federal de Santa Maria Centro de Ciências Rurais |
dc.source.none.fl_str_mv |
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