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dc.creatorSilva, Estevão Lucas Ramos da
dc.date.accessioned2024-01-08T17:08:19Z
dc.date.available2024-01-08T17:08:19Z
dc.date.issued2023-12-21
dc.identifier.citationSILVA, Estevão Lucas Ramos da. CRISP-DM no desenvolvimento de funções de pedotransferência: um estudo de caso com o banco de dados HYBRAS. Orientador: Antônio Correia de Sá Barreto Neto. 2023. Artigo (Tecnólogo em Análise e Desenvolvimento de Sistemas) - Instituto Federal de Educação, Ciência e Tecnologia de Pernambuco - Campus Paulista, Paulista, PE, 2023. 44 p.pt_BR
dc.identifier.urihttps://repositorio.ifpe.edu.br/xmlui/handle/123456789/1141
dc.description.abstractThis research describes a detailed analysis on the development of pedotransfer functions to estimate Field Capacity and Permanent Wilting Point. The adopted approach utilizes data from the HYBRAS database, which holds information on soil hydraulic constants, and follows the CRISP-DM methodology, providing a standardized framework for model development. The study involves the construction of twelve artificial intelligence models, exploring algorithms that seek both linear and non-linear relationships. The Gradient Boosting algorithm demonstrated the best performance in estimating the Permanent Wilting Point, achieving a coefficient of determination (R²) of 0.74 and a Root Mean Square Error (RMSE) of 0.04 cm³/cm³. The project emphasizes the intention to empower experts during the development of the functions, highlighting the relevance of active participation of these professionals throughout all stages of the process.pt_BR
dc.format.extent44 p.pt_BR
dc.languagept_BRpt_BR
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dc.rightsAcesso Abertopt_BR
dc.rightsAn error occurred on the license name.*
dc.rights.uriAn error occurred getting the license - uri.*
dc.subjectCRISP-DMpt_BR
dc.subjectHybraspt_BR
dc.subjectFunções de Pedotransferênciapt_BR
dc.subjectUmidade do solo e Mineração de Dadospt_BR
dc.titleCRISP-DM no desenvolvimento de funções de pedotransferência: um estudo de caso com o Banco de Dados HYBRASpt_BR
dc.typeArticlept_BR
dc.creator.Latteshttp://lattes.cnpq.br/0070636576760827pt_BR
dc.contributor.advisor1Barreto Neto, Antônio Correia de Sá
dc.contributor.advisor1Latteshttp://lattes.cnpq.br/2773609778338983pt_BR
dc.contributor.referee1Queiroz, Anderson Apolônio de Lira
dc.contributor.referee2Barros, Alexandre Hugo Cezar
dc.contributor.referee1Latteshttp://lattes.cnpq.br/ 0652960425058437pt_BR
dc.contributor.referee2Latteshttp://lattes.cnpq.br/ 7312779971785780pt_BR
dc.publisher.departmentPaulistapt_BR
dc.publisher.countryBrasilpt_BR
dc.subject.cnpqCIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::SISTEMAS DE COMPUTACAOpt_BR
dc.description.resumoEsta pesquisa descreve uma análise detalhada sobre a criação de funções de pedotransferência para estimar a Capacidade de Campo e o Ponto de Murcha Permanente. A abordagem adotada utiliza dados provenientes do banco HYBRAS que detém informações de constantes hidráulicas do solo, e segue a metodologia CRISP-DM, no qual oferece uma estrutura padronizada para o desenvolvimento de modelos. O estudo envolve a construção de doze modelos de inteligência artificial, explorando algoritmos que buscam relações tanto lineares quanto n˜ao lineares. O algoritmo de Gradient Boosting demonstrou o melhor desempenho para estimar o Ponto de Murcha Permanente, alcançando um coeficiente de determinação (R2) de 0.74 e um Erro Quadrático Médio (RMSE) de 0.04 cm3/cm3. O projeto destaca a intenção de dar protagonismo aos especialistas durante o desenvolvimento das funções, ressaltando a relevância da participação ativa desses profissionais ao longo de todas as etapas do processo.pt_BR


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