Modelling and prediction of major soil chemical properties with Random Forest: Machine learning as tool to understand soil-environment relationships in Antarctica-Reference-Cited by-同舟云学术

Modelling and prediction of major soil chemical properties with Random Forest: Machine learning as tool to understand soil-environment relationships in Antarctica

Published:2024-02 Issue: Volume:235 Page:107677
ISSN:0341-8162
Container-title:CATENA
language:en
Short-container-title:CATENA

Author:

Siqueira Rafael G.^ORCID,Moquedace Cássio M.^ORCID,Fernandes-Filho Elpídio I.^ORCID,Schaefer Carlos E.G.R.,Francelino Márcio R.,Sacramento Iorrana F.,Michel Roberto F.M.^ORCID

Funder

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Publisher

Elsevier BV

Subject

Earth-Surface Processes

Reference143 articles.

1. Content of available forms of nitrogen, potassium, and phosphorus in ornithogenic and other soils of the Fildes Peninsula (King George Island, Western Antarctica);Abakumov;Biological Communications,2018

2. The influence of brown and south polar skua on the content of plant nutrient in the soils from the Fildes Peninsula (King George Island, West Antarctica);Abakumov;Chemistry and Ecology,2021

3. High-Resolution 3-D Mapping of Soil Texture in Denmark;Adhikari;Soil Science Society of America Journal,2013

4. The content and distribution of trace elements and polycyclic aromatic hydrocarbons in soils of Maritime Antarctica;Alekseev;Environ Monit Assess,2020

5. Clay mineralogy and micropedology of phosphate-rich soils from Lions Rump, Maritime Antarctica;Almeida;J South Am Earth Sci,2021

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