ARTIFICIAL NEURAL NETWORKS (ANN) FOR HEIGHT ESTIMATION IN A MIXED-SPECIES PLANTATION OF <i>Eucalyptus globulus</i> LABILL AND <i>Acacia mearnsii</i> DE WILD

Soares, Gustavo Martins; Silva, Luciana Duque; Higa, Antonio Rioyei; Simon, Augusto Arlindo; José, Jackson Freitas Brilhante de São

doi:10.1590/1806-908820210000012

Acessibilidade / Reportar erro

Brasil

Español English

sumário « anterior atual seguinte »

Sumário

Scientific Article • Rev. Árvore 45 • 2021 • https://doi.org/10.1590/1806-908820210000012 copy

ARTIFICIAL NEURAL NETWORKS (ANN) FOR HEIGHT ESTIMATION IN A MIXED-SPECIES PLANTATION OF Eucalyptus globulus LABILL AND Acacia mearnsii DE WILD

REDES NEURAIS ARTIFICIAS (RNA) PARA ESTIMATIVA DE ALTURA EM UM PLANTIO MISTO DE Eucalyptus globulus LABILL E Acacia mearnsii DE WILD

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

The objective of this study is to evaluate the fit of Artificial Neural Networks (ANN) for height estimation and evaluation of the effects of consortium in a mixed-species plantation of Eucalyptus globulus (E) and Acacia mearnsii (A). The experiment was installed in 2005, on two farms in the municipality of Piratini - RS, where was planted the species Eucalyptus globulus (E) and Acacia mearnsii (A), in monoculture and mixed in simple lines (50%E:50%A - SL), and double lines (50%E:50%A - DL). The training and evaluation of the networks were made in R-project with the package neuralnet. All ANNs, from the simplest to the most complex, showed high values for Rŷy and low for Syx, BIAS and RMSE, with superior results in ANN 3, 4, and 6, which demonstrates that the information of DBHmin, DBHmean, and DBHmax were important stand attributes. Furthermore, the ANNs were able to capture the different growth patterns shown by the species in the different forms of consortiums, therefore is indicated for the height estimation in monocultures and mixed plantations of Eucalyptus globulus and Acacia mearnsii, and only one ANN would be necessary to represent the entire population.

Keywords:
Artificial Intelligence; Hypsometric Relationship; Consortium

Table 1 Characteristics of artificial neural networks (ANN) used to estimate the height of the trees, as well as the values of the statistics used for the selection of the models that best fit the data.

ANN	Categorical Input	Continuous Input	Architecture	R_{y y}	S_yx	BIAS	RMSE
1	Treatment, Species	DBH	8-4-1	0.962	1.50	-0.003	11.18
2	Treatment, Species	DBH	8-4-2-1	0.962	1.50	-0.004	11.18
3	Treatment, Species	DBH, DBHmean, DBHmax, DBHmin	8-4-1	0.984	0.98	0.003	7.30
4	Treatment, Species	DBH, DBHmean, DBHmax, DBHmin	8-4-2-1	0.984	0.99	0.002	7.37
5	Treatment, Species	DBH, DBHmean, DBHmax, DBHmin, Age	8-4-1	0.984	0.99	0.002	7.37
6	Treatment, Species	DBH, DBHmean, DBHmax, DBHmin, Age	8-4-2-1	0.984	0.97	0.003	7.24
7	Treatment, Species	DBH, DH	8-4-1	0.983	1.01	0.001	7.52
8	Treatment, Species	DBH, DH	8-4-2-1	0.983	1.01	0.001	7.51
9	Treatment, Species	DBH, Age	8-4-1	0.978	1.14	0.002	8.47
10	Treatment, Species	DBH, Age	8-4-2-1	0.978	1.13	0.005	8.45
11	Treatment, Species	DBH, DH, Age	8-4-1	0.983	1.00	0.002	7.46
12	Treatment, Species	DBH, DH, Age	8-4-2-1	0.983	1.00	0.002	7.44
13	Treatment, Species	DBH, DH, Age, SA	8-4-1	0.983	1.00	0.002	7.43
14	Treatment, Species	DBH, DH, Age, SA	8-4-2-1	0.983	1.00	0.002	7.44

Table 2 Dispersion of the percentage errors for the training and validation dataset of the best ANN's tested.

ANN	Dispersion of the percentage errors - Training dataset	Dispersion of the percentage errors - Validation dataset
3
4
6
14

Sociedade de Investigações Florestais Universidade Federal de Viçosa, CEP: 36570-900 - Viçosa - Minas Gerais - Brazil, Tel: (55 31) 3612-3959 - Viçosa - MG - Brazil
E-mail: rarvore@sif.org.br

Acompanhe os números deste periódico no seu leitor de RSS

[1] *Corresponding author.