Use este identificador para citar ou linkar para este item: 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
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