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.
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.
the Creative Commons Attribution 4.0 License.
Methods for assessment of models
| 
10 Feb 2022
Methods for assessment of models |
| 10 Feb 2022
| 10 Feb 2022
A new methodological framework for geophysical sensor combinations associated with machine learning algorithms to understand soil attributes
Danilo César de Mello
Department of Soil Science, Federal University of Viçosa, Viçosa, Brazil
Gustavo Vieira Veloso
Department of Soil Science, Federal University of Viçosa, Viçosa, Brazil
Marcos Guedes de Lana
Department of Soil Science, Federal University of Viçosa, Viçosa, Brazil
Fellipe Alcantara de Oliveira Mello
Department of Soil Science, Luiz de Queiroz College of
Agriculture, University of São Paulo, Av. Pádua Dias, 11, CP 9,
Piracicaba, SP 13418-900, Brazil
Raul Roberto Poppiel
Department of Soil Science, Luiz de Queiroz College of
Agriculture, University of São Paulo, Av. Pádua Dias, 11, CP 9,
Piracicaba, SP 13418-900, Brazil
Diego Ribeiro Oquendo Cabrero
Geography Department of Federal University of Mato Grosso do Sul, Av. Ranulpho Marques Leal, no. 3484, Distrito Industrial CEP
79610-100 Três Lagoas/MS, Brazil
Luis Augusto Di Loreto Di Raimo
Department of Geology and Natural Resources, Institute of
Geosciences, University of Campinas, Rua Carlos Gomes, 250, Cidade
Universitária, CEP 13083-855, Campinas/SP, Brazil
Carlos Ernesto Gonçalves Reynaud Schaefer
Department of Soil Science, Federal University of Viçosa, Viçosa, Brazil
Elpídio Inácio Fernandes Filho
Department of Soil Science, Federal University of Viçosa, Viçosa, Brazil
Emilson Pereira Leite
Department of Geology and Natural Resources, Institute of
Geosciences, University of Campinas, Rua Carlos Gomes, 250, Cidade
Universitária, CEP 13083-855, Campinas/SP, Brazil
José Alexandre Melo Demattê
CORRESPONDING AUTHOR
Department of Soil Science, Luiz de Queiroz College of
Agriculture, University of São Paulo, Av. Pádua Dias, 11, CP 9,
Piracicaba, SP 13418-900, Brazil
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The study explores Maritime Antarctica's geology, shaped by periglacial forces, using pioneering gamma-spectrometric and magnetic surveys on igneous rocks due to limited Antarctic surveys. Machine learning predicts radionuclide and magnetic content based on terrain features, linking their distribution to landscape processes, morphometrics, lithology, and pedogeomorphology. Inaccuracies arise due to complex periglacial processes and landscape complexities.
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Revised manuscript not accepted
Short summary
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We proposed a different method to evaluate different intensities of weathering in a heterogeneous area (soils, geology and relief) and small number of samples. We use combined data from three geophysical sensors, clustering and machine learning (nested-leave-one-out-cross-validation) to distinguish weathering intensities and assess the relationship of these variables with weathering, relief, geology, and soil types and attributes. and we obtained satisfactory performances of models evaluation.
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The number of samples utilised to create predictive models affected model performance. This research compares the number of samples needed by a deep learning model to outperform the traditional machine learning models using visible near-infrared spectroscopy data for soil properties predictions. The deep learning model was found to outperform machine learning models when the sample size was above 2000.
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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.
We used soil parent material, terrain attributes, and geophysical data from the soil surface to...
