Artigo de periodico

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
• 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:
  • Financiamento CNPq
  • Financiamento FAPESP
• Language: Inglês
• Imprenta:
  • Publisher place: Göttingen
  • Date published: 2022
• Source:
  • Título: Geoscientific Model Development
  • ISSN: 1991-9603
  • Volume/Número/Paginação/Ano: v. 15, p. 1219–1246, 2022


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• 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: 29 mar. 2026.

• 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 2026 mar. 29 ] 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 2026 mar. 29 ] Available from: https://doi.org/10.5194/gmd-15-1219-2022

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