Evaluation of machine learning algorithms to classify and map landforms in Antarctica

Author:

Siqueira Rafael G.1ORCID,Veloso Gustavo V.1ORCID,Fernandes‐Filho Elpídio I.1ORCID,Francelino Márcio R.1ORCID,Schaefer Carlos Ernesto G. R.1ORCID,Corrêa Guilherme R.2ORCID

Affiliation:

1. Department of Soils Federal University of Viçosa Av. PH Rolfs s/n Viçosa MG Brazil

2. Institute of Geography Federal University of Uberlândia Av. João Naves de Ávila 2121 Uberlândia MG Brazil

Funder

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Publisher

Wiley

Subject

Earth and Planetary Sciences (miscellaneous),Earth-Surface Processes,Geography, Planning and Development

Reference87 articles.

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2. Novel ensembles of COPRAS multi‐criteria decision‐making with logistic regression, boosted regression tree, and random forest for spatial prediction of gully erosion susceptibility;Arabameri A.;Science of the Total Environment,2019

3. Random forests;Breiman L.;Machine Learning,2001

4. Statistical geocomputing combining R and SAGA: The example of landslide susceptibility analysis with generalized additive models;Brenning A.;SAGA ‐‐Seconds Out (Hamburger Beiträge zur Physischen Geographie und Landschaftsökologie),2008

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