Artificial neural networks and adaptive neuro-fuzzy inference systems for prediction of soil respiration in forested areas southern Brazil

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorVicentini, Maria Elisa-
Autor(es): dc.creatorda Silva, Paulo Alexandre-
Autor(es): dc.creatorCanteral, Kleve Freddy Ferreira-
Autor(es): dc.creatorDe Lucena, Wanderson Benerval-
Autor(es): dc.creatorde Moraes, Mario Luiz Teixeira-
Autor(es): dc.creatorMontanari, Rafael-
Autor(es): dc.creatorFilho, Marcelo Carvalho Minhoto Teixeira-
Autor(es): dc.creatorPeruzzi, Nelson José-
Autor(es): dc.creatorLa Scala, Newton-
Autor(es): dc.creatorDe Souza Rolim, Glauco-
Autor(es): dc.creatorPanosso, Alan Rodrigo-
Data de aceite: dc.date.accessioned2025-08-21T19:59:29Z-
Data de disponibilização: dc.date.available2025-08-21T19:59:29Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2023-09-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1007/s10661-023-11679-8-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/300901-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/300901-
Descrição: dc.descriptionThe purpose of this study was to estimate the temporal variability of CO2 emission (FCO2) from O2 influx into the soil (FO2) in a reforested area with native vegetation in the Brazilian Cerrado, as well as to understand the dynamics of soil respiration in this ecosystem. The database is composed of soil respiration data, agroclimatic variables, improved vegetation index (EVI), and soil attributes used to train machine learning algorithms: artificial neural network (ANN) and an adaptive neuro-fuzzy inference system (ANFIS). The predictive performance was evaluated based on the mean absolute error (MEA), root mean square error (RMSE), mean absolute percentage error (MAPE), agreement index (d), confidence coefficient (c), and coefficient of determination (R 2). The best estimation results for validation were FCO2 with multilayer perceptron neural network (MLP) (R 2 = 0.53, RMSE = 0.967 µmol m−2 s−1) and radial basis function neural network (RBF) (R 2 = 0.54, RMSE = 0.884 µmol m−2 s−1) and FO2 with MLP (R 2 = 0.45, RMSE = 0.093 mg m−2 s−1) and RBF (R 2 = 0.74, 0.079 mg m−2 s−1). Soil temperature and macroporosity are important predictors of FCO2 and FO2. The best combination of variables for training the ANFIS was selected based on trial and error. The results were as follows: FCO2 (R 2 = 16) and FO2 (R 2 = 29). In all models, FCO2 outperformed FO2. A primary factor analysis was performed, and FCO2 and FO2 correlated best with the weather and soil attributes, respectively.-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)-
Descrição: dc.descriptionDepartment Engineering and Exact Sciences School of Agricultural and Veterinarian Sciences São Paulo State University (FCAV/UNESP), Via de Acesso Prof. Paulo Donato Castellane S/N-
Descrição: dc.descriptionDepartment of Phytotecnics Faculty of Engineer (FEIS/UNESP), Avenida Brasil–Centro-
Descrição: dc.descriptionDepartment of Phytosanity Rural Engineering and Soils Faculty of Engineer (FEIS/UNESP), Avenida Brasil–Centro-
Descrição: dc.descriptionDepartment Engineering and Exact Sciences School of Agricultural and Veterinarian Sciences São Paulo State University (FCAV/UNESP), Via de Acesso Prof. Paulo Donato Castellane S/N-
Descrição: dc.descriptionDepartment of Phytotecnics Faculty of Engineer (FEIS/UNESP), Avenida Brasil–Centro-
Descrição: dc.descriptionDepartment of Phytosanity Rural Engineering and Soils Faculty of Engineer (FEIS/UNESP), Avenida Brasil–Centro-
Descrição: dc.descriptionFAPESP: 2016/03861-5-
Descrição: dc.descriptionCAPES: Code 001-
Idioma: dc.languageen-
Relação: dc.relationEnvironmental Monitoring and Assessment-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectArtificial intelligence-
Palavras-chave: dc.subjectOxygen influx-
Palavras-chave: dc.subjectReforestation, Tropical ecosystems-
Palavras-chave: dc.subjectSoil CO2 emission-
Título: dc.titleArtificial neural networks and adaptive neuro-fuzzy inference systems for prediction of soil respiration in forested areas southern Brazil-
Tipo de arquivo: dc.typelivro digital-
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