气候变化领域集成服务门户(CCPortal): Integration of principal component analysis and artificial neural network to modeling productive capacity of eucalypt stands from biophysical attributes

CCPortal

DOI	10.1016/j.foreco.2019.117862
	Integration of principal component analysis and artificial neural network to modeling productive capacity of eucalypt stands from biophysical attributes
	Dolácio C.J.F.; Oliveira R.S.; Nakajima N.Y.; Tavares Júnior I.D.S.; Rocha J.E.C.D.; Ebling Â.A.; Gama M.A.P.
发表日期	2020
ISSN	0378-1127
卷号	460
英文摘要	Modeling the productive capacity of forest sites from biophysical factors is important when site-dominant height data is not available. For this reason, we aim with this study to model the mean annual volume increment at age 7 (MAI7) of Eucalyptus clonal plantations, to evaluate the accuracy of the modeling, and to fit an empirical equation. For this, we used data from twenty-two variables collected in 51 plots distributed randomly in three classes of MAI7 predicted. Initially, Spearman's rank correlation was used for primary mining of these variables, then principal components analysis (PCA) was used to create orthogonal latent variables that were used as input in the artificial neural network (ANN) to MAI7 predict. Spearman's and PCA analysis proved to be excellent for data mining because when used together were enabled to reduce the number of variables and create a variable that represented the maximum variance of the variables which were significantly associated with the MAI7. All ANN trained exhibited high learning capability when using as input to the latent variables created in conjunction with the classes of MAI7, but the ANN trained with architecture 6-9-1 made prediction with greater precision for the test set and too showed high accuracy to the train set. Therefore, combining PCA with ANN was an excellent approach to developing an empirical equation to accurately predict MAI7 of clonal plantings of hybrid of Eucalyptus located in the southeast of Pará State, Brazil from biophysical variables. © 2020 Elsevier B.V.
英文关键词	Eucalypt in Brazilian Amazon; Forest growth; Multivariate analysis; Potential productivity; Univariate analysis
语种	英语
scopus关键词	Biophysics; Data mining; Forecasting; Forestry; Multivariant analysis; Neural networks; Brazilian Amazon; Forest growth; Multi variate analysis; Potential productivity; Univariate analysis; Principal component analysis; artificial neural network; biophysics; clonal organism; data mining; forestry production; multivariate analysis; plantation forestry; precision; principal component analysis; productivity; stand structure; Analysis; Capacity; Data; Forecasts; Forestry; Neural Networks; Podocarpus Spicatus; Variables; Amazonia; Brazil; Eucalyptus
来源期刊	Forest Ecology and Management
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/155473
作者单位	Department of Forest Sciences, Federal University of Paraná, Curitiba, Paraná, Brazil; Department of Forest Sciences, Federal University of Viçosa, Viçosa, Minas Gerais, Brazil; Federal Rural University of Amazônia, ParagominasPará, Brazil; Federal Rural University of Amazônia, ParauapebasPará, Brazil; Federal Rural University of Amazônia, Belém, Pará, Brazil
推荐引用方式 GB/T 7714	Dolácio C.J.F.,Oliveira R.S.,Nakajima N.Y.,et al. Integration of principal component analysis and artificial neural network to modeling productive capacity of eucalypt stands from biophysical attributes[J],2020,460.
APA	Dolácio C.J.F..,Oliveira R.S..,Nakajima N.Y..,Tavares Júnior I.D.S..,Rocha J.E.C.D..,...&Gama M.A.P..(2020).Integration of principal component analysis and artificial neural network to modeling productive capacity of eucalypt stands from biophysical attributes.Forest Ecology and Management,460.
MLA	Dolácio C.J.F.,et al."Integration of principal component analysis and artificial neural network to modeling productive capacity of eucalypt stands from biophysical attributes".Forest Ecology and Management 460(2020).