Machine learning for prediction of soil CO2 emission in tropical forests in the Brazilian Cerrado

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorCanteral, Kleve Freddy Ferreira-
Autor(es): dc.creatorVicentini, Maria Elisa-
Autor(es): dc.creatorde Lucena, Wanderson Benerval-
Autor(es): dc.creatorde Moraes, Mário Luiz Teixeira-
Autor(es): dc.creatorMontanari, Rafael-
Autor(es): dc.creatorFerraudo, Antonio Sergio-
Autor(es): dc.creatorPeruzzi, Nelson José-
Autor(es): dc.creatorLa Scala, Newton-
Autor(es): dc.creatorPanosso, Alan Rodrigo-
Data de aceite: dc.date.accessioned2025-08-21T22:48:09Z-
Data de disponibilização: dc.date.available2025-08-21T22:48:09Z-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2023-05-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1007/s11356-023-26824-6-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/248684-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/248684-
Descrição: dc.descriptionSoil CO2 emission (FCO2) is a critical component of the global carbon cycle, but it is a source of great uncertainty due to the great spatial and temporal variability. Modeling of soil respiration can strongly contribute to reducing the uncertainties associated with the sources and sinks of carbon in the soil. In this study, we compared five machine learning (ML) models to predict the spatiotemporal variability of FCO2 in three reforested areas: eucalyptus (RE), pine (RP) and native species (RNS). The study also included a generalized scenario (GS) where all the data from RE, RP and RNS were included in one dataset. The ML models include generalized regression neural network (GRNN), radial basis function neural network (RBFNN), multilayer perceptron neural network (MLPNN), adaptive neuro-fuzzy inference system (ANFIS) and random forest (RF). Initially, we had 32 attributes and after pre-processing, including Pearson’s correlation, canonical correlation analysis (CCA), and biophysical justification, only 21 variables remained. We used as input variables 19 soil properties and climate variables in reforested areas of eucalyptus, pine and native species. RF was the best model to predict soil respiration to RE [adjusted coefficient of determination (R2 adj): 0.70 and root mean square error (RMSE): 1.02 µmol m−2 s−1], RP (R2 adj: 0.48 and RMSE: 1.07 µmol m−2 s−1) and GS (R2 adj: 0.70 and RMSE: 1.05 µmol m−2 s−1). Our findings support that RF and GRNN are promising for predicting soil respiration of reforested areas which could help to identify and monitor potential sources and sinks of the main additional greenhouse gas over ecosystems. Graphical abstract: [Figure not available: see fulltext.]-
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, São Paulo-
Descrição: dc.descriptionDepartment of Phytotecnics Faculty of Engineer (FEIS/UNESP), Avenida Brasil – Centro, São Paulo-
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, São Paulo-
Descrição: dc.descriptionDepartment of Phytotecnics Faculty of Engineer (FEIS/UNESP), Avenida Brasil – Centro, São Paulo-
Descrição: dc.descriptionCAPES: 001-
Formato: dc.format61052-61071-
Idioma: dc.languageen-
Relação: dc.relationEnvironmental Science and Pollution Research-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectClimate change-
Palavras-chave: dc.subjectEnvironmental modeling-
Palavras-chave: dc.subjectSoil respiration-
Palavras-chave: dc.subjectTropical ecosystems-
Título: dc.titleMachine learning for prediction of soil CO2 emission in tropical forests in the Brazilian Cerrado-
Tipo de arquivo: dc.typelivro digital-
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