A new methodological framework for geophysical sensor combinations associated with machine learning algorithms to understand soil attributes (2022)
- Authors:
- Mello, Danilo César de
- Veloso, Gustavo Vieira
- Lana, Marcos Guedes de
- Mello, Fellipe Alcantara de Oliveira
- Poppiel, Raul Roberto
- Cabrero, Diego Ribeiro Oquendo
- Di Raimo, Luis Augusto Di Loreto
- Schaefer, Carlos Ernesto Gonçalves Reynaud
- Fernandes Filho, Elpídio Inácio
- Leite, Emilson Pereira
- Demattê, José Alexandre Melo
- Mello, Danilo César de
- USP affiliated authors: DEMATTE, JOSE ALEXANDRE MELO - ESALQ ; MELLO, FELLIPE ALCANTARA DE OLIVEIRA - ESALQ ; POPPIEL, RAUL ROBERTO - ESALQ
- Unidade: ESALQ
- DOI: 10.5194/gmd-15-1219-2022
- Subjects: ALGORITMOS; APRENDIZADO COMPUTACIONAL; FRAMEWORKS; GEOFÍSICA; MODELAGEM DE DADOS; SENSOR; SOLOS
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título: Geoscientific Model Development
- ISSN: 1991-9603
- Volume/Número/Paginação/Ano: v. 15, p. 1219–1246, 2022
- Este periódico é de acesso aberto
- Este artigo é de acesso aberto
- URL de acesso aberto
- Cor do Acesso Aberto: gold
- Licença: cc-by
-
ABNT
MELLO, Danilo César de et al. A new methodological framework for geophysical sensor combinations associated with machine learning algorithms to understand soil attributes. Geoscientific Model Development, v. 15, p. 1219–1246, 2022Tradução . . Disponível em: https://doi.org/10.5194/gmd-15-1219-2022. Acesso em: 04 out. 2024. -
APA
Mello, D. C. de, Veloso, G. V., Lana, M. G. de, Mello, F. A. de O., Poppiel, R. R., Cabrero, D. R. O., et al. (2022). A new methodological framework for geophysical sensor combinations associated with machine learning algorithms to understand soil attributes. Geoscientific Model Development, 15, 1219–1246. doi:10.5194/gmd-15-1219-2022 -
NLM
Mello DC de, Veloso GV, Lana MG de, Mello FA de O, Poppiel RR, Cabrero DRO, Di Raimo LADL, Schaefer CEGR, Fernandes Filho EI, Leite EP, Demattê JAM. A new methodological framework for geophysical sensor combinations associated with machine learning algorithms to understand soil attributes [Internet]. Geoscientific Model Development. 2022 ; 15 1219–1246.[citado 2024 out. 04 ] Available from: https://doi.org/10.5194/gmd-15-1219-2022 -
Vancouver
Mello DC de, Veloso GV, Lana MG de, Mello FA de O, Poppiel RR, Cabrero DRO, Di Raimo LADL, Schaefer CEGR, Fernandes Filho EI, Leite EP, Demattê JAM. A new methodological framework for geophysical sensor combinations associated with machine learning algorithms to understand soil attributes [Internet]. Geoscientific Model Development. 2022 ; 15 1219–1246.[citado 2024 out. 04 ] Available from: https://doi.org/10.5194/gmd-15-1219-2022 - Sand fractions micromorphometry detected by Vis-NIR-MIR and its impact on water retention
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Informações sobre o DOI: 10.5194/gmd-15-1219-2022 (Fonte: oaDOI API)
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