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https://locus.ufv.br//handle/123456789/23764
Tipo: | Artigo |
Título: | Artificial neural networks: Modeling tree survival and mortality in the Atlantic Forest biome in Brazil |
Autor(es): | Rocha, Samuel José Silva Soares da Torres, Carlos Moreira Miquelino Eleto Jacovine, Laércio Antônio Gonçalves Leite, Helio Garcia Schettini, Bruno Leão Said Villanova, Paulo Henrique Zanuncio, José Cola Gelcer, Eduardo Monteiro Silva, Liniker Fernandes da Reis, Leonardo Pequeno |
Abstract: | Models to predict tree survival and mortality can help to understand vegetation dynamics and to predict effects of climate change on native forests. The objective of the present study was to use Artificial Neural Networks, based on the competition index and climatic and categorical variables, to predict tree survival and mortality in Semideciduous Seasonal Forests in the Atlantic Forest biome. Numerical and categorical trees variables, in permanent plots, were used. The Agricultural Reference Index for Drought (ARID) and the distance-dependent competition index were the variables used. The overall efficiency of classification by ANNs was higher than 92% and 93% in the training and test, respectively. The accuracy for classification and number of surviving trees was above 99% in the test and in training for all ANNs. The classification accuracy of the number of dead trees was low. The mortality accuracy rate (10.96% for training and 13.76% for the test) was higher with the ANN 4, which considers the climatic variable and the competition index. The individual tree-level model integrates dendrometric and meteorological variables, representing a new step for modeling tree survival in the Atlantic Forest biome. |
Palavras-chave: | Artificial intelligence Prognosis Tropical forests |
Editor: | Science of The Total Environment |
Tipo de Acesso: | Elsevier B. V. |
URI: | https://doi.org/10.1016/j.scitotenv.2018.07.123 http://www.locus.ufv.br/handle/123456789/23764 |
Data do documento: | 15-Dez-2018 |
Aparece nas coleções: | Engenharia Florestal - Artigos |
Arquivos associados a este item:
Arquivo | Descrição | Tamanho | Formato | |
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artigo.pdf Until 2100-12-31 | Texto completo | 1,16 MB | Adobe PDF | Visualizar/Abrir ACESSO RESTRITO |
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