Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 15, issue 3
Articles | Volume 15, issue 3
https://doi.org/10.5194/gmd-15-1219-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-1219-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 3
Methods for assessment of models
 | 
10 Feb 2022
Methods for assessment of models |  | 10 Feb 2022

A new methodological framework for geophysical sensor combinations associated with machine learning algorithms to understand soil attributes

Danilo César de Mello, Gustavo Vieira Veloso, Marcos Guedes de Lana, Fellipe Alcantara de Oliveira Mello, Raul Roberto Poppiel, Diego Ribeiro Oquendo Cabrero, Luis Augusto Di Loreto Di Raimo, Carlos Ernesto Gonçalves Reynaud Schaefer, Elpídio Inácio Fernandes Filho, Emilson Pereira Leite, and José Alexandre Melo Demattê

Supplement

https://doi.org/10.5194/gmd-15-1219-2022-supplement

Data sets

Data and script for "A new methodological framework for geophysical sensor combinations associated with machine learning algorithms to understand soil attributes" Gustavo Vieira Veloso, Danilo César de Mello, Marcos Guedes Lana, Fellipe Alcantara de Oliveira Mello, Raúl Roberto Poppiel, Diego Ribeiro Oquendo Cabrero, Luis Augusto Di Raimo, Carlos Ernesto Gonçalves Reynaud Schaefer, Elpídio Inácio Fernandes-Filho, Emilson Pereira Leite, and José Alexandre Melo Demattê https://doi.org/10.5281/zenodo.5733366

Model code and software

Data and script for "A new methodological framework by geophysical sensors combinations associated with machine learning algorithms to understand soil attributes" Gustavo Vieira Veloso, Danilo César de Mello, Marcos Guedes Lana, Fellipe Alcantara de Oliveira Mello, Raúl Roberto Poppiel, Diego Ribeiro Oquendo Cabrero, Luis Augusto Di Raimo, Carlos Ernesto Gonçalves Reynaud Schaefer, Elpídio Inácio Fernandes-Filho, Emilson Pereira Leite, and José Alexandre Melo Demattê https://doi.org/10.5281/zenodo.5733366

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Short summary
We used soil parent material, terrain attributes, and geophysical data from the soil surface to test and compare different and unprecedented geophysical sensor combination, as well as different machine learning algorithms to model and predict several soil attributes. Also, we analyzed the importance of pedoenvironmental variables. The soil attributes were modeled throughout different machine learning algorithms and related to different geophysical sensor combinations.
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