Volumetric estimates in eucalyptus stands using support vector machines and artificial neural networks
This study aimed to evaluate the performance of artificial neural networks (ANN) and support vector machines (SVM) in volumetric modeling in eucalyptus stands. Data from commercial plantations, located in four municipalities in the southern mesoregion of the state of Amapá, were used and were provid...
| Hlavní autoři: | , , , , , , |
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| Médium: | Online |
| Jazyk: | spa |
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Instituto de Ecología, A.C.
2022
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| On-line přístup: | https://myb.ojs.inecol.mx/index.php/myb/article/view/2252 |
| _version_ | 1799769419962908672 |
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| author | Cordeiro, Marcio Assis Arce, Julio Eduardo Guimarães, Fabiane Aparecida Retslaff Bonete, Izabel Passos Silva, Anthoinny Vittória dos Santos de Abreu, Jadson Coelho Binoti, Daniel Henrique Breda |
| author_facet | Cordeiro, Marcio Assis Arce, Julio Eduardo Guimarães, Fabiane Aparecida Retslaff Bonete, Izabel Passos Silva, Anthoinny Vittória dos Santos de Abreu, Jadson Coelho Binoti, Daniel Henrique Breda |
| author_sort | Cordeiro, Marcio Assis |
| collection | MYB |
| description | This study aimed to evaluate the performance of artificial neural networks (ANN) and support vector machines (SVM) in volumetric modeling in eucalyptus stands. Data from commercial plantations, located in four municipalities in the southern mesoregion of the state of Amapá, were used and were provided by a private company. Volumetric models established in the literature were adjusted and compared with the SVM and ANN techniques. Data were divided into 80% for training and 20% for model validation. The same dendometric variables used by the regression models (DBH and height) were used by the SVM and ANN. For training and generalization of the SVM, four configurations were used, formed from two error functions and two Kernel functions. For configuration, training, and generalization of the ANN, the NeuroForest-Volumetric software was used, in which network configurations such as Adaline (Adaptive Linear Element) were used; Multilayer Perceptron (MLP) and Radial Base Functions (RBF). The quality of the adjustments of the regression models, and of the methodologies using ANN and SVM, were evaluated using the correlation coefficient between the observed and estimated individual volumes (ryŷ), the root mean square error, expressed as a percentage of the mean (RMSE%), and graphical analysis of residues (Res%). Considering the results, SVM and ANN performed slightly better, compared to the traditional methodology, in individual volume estimates, demonstrating that they are techniques that are well suited for applications in the area of measurement and forest management. |
| format | Online |
| id | oai:oai.myb.ojs.inecol.mx:article-2252 |
| institution | Madera y Bosques |
| language | spa |
| publishDate | 2022 |
| publisher | Instituto de Ecología, A.C. |
| record_format | ojs |
| spelling | oai:oai.myb.ojs.inecol.mx:article-22522022-11-29T22:15:05Z Volumetric estimates in eucalyptus stands using support vector machines and artificial neural networks Estimativas volumétricas em povoamentos de eucalipto utilizando máquinas de vetores de suporte e redes neurais artificiais Cordeiro, Marcio Assis Arce, Julio Eduardo Guimarães, Fabiane Aparecida Retslaff Bonete, Izabel Passos Silva, Anthoinny Vittória dos Santos de Abreu, Jadson Coelho Binoti, Daniel Henrique Breda regression analysis machine learning volumetry análise de regressão aprendizado de máquina volumetria This study aimed to evaluate the performance of artificial neural networks (ANN) and support vector machines (SVM) in volumetric modeling in eucalyptus stands. Data from commercial plantations, located in four municipalities in the southern mesoregion of the state of Amapá, were used and were provided by a private company. Volumetric models established in the literature were adjusted and compared with the SVM and ANN techniques. Data were divided into 80% for training and 20% for model validation. The same dendometric variables used by the regression models (DBH and height) were used by the SVM and ANN. For training and generalization of the SVM, four configurations were used, formed from two error functions and two Kernel functions. For configuration, training, and generalization of the ANN, the NeuroForest-Volumetric software was used, in which network configurations such as Adaline (Adaptive Linear Element) were used; Multilayer Perceptron (MLP) and Radial Base Functions (RBF). The quality of the adjustments of the regression models, and of the methodologies using ANN and SVM, were evaluated using the correlation coefficient between the observed and estimated individual volumes (ryŷ), the root mean square error, expressed as a percentage of the mean (RMSE%), and graphical analysis of residues (Res%). Considering the results, SVM and ANN performed slightly better, compared to the traditional methodology, in individual volume estimates, demonstrating that they are techniques that are well suited for applications in the area of measurement and forest management. Este estudo teve por objetivo avaliar o desempenho de redes neurais artificiais (RNA) e máquinas de vetor de suporte (MVS) na modelagem volumétrica em povoamentos de eucalipto. Utilizou-se dados oriundos de plantios comerciais não desbastados, localizados em quatro municípios na mesorregião sul do estado do Amapá e foram disponibilizados por uma empresa privada. Foram ajustados modelos volumétricos consagrados na literatura e comparados com a técnica de MVS e de RNA. Os dados foram divididos em 80% para treinamento e 20% para validação dos modelos, as mesmas variáveis dendrométricas utilizadas pelos modelos de regressão (dap e altura) foram utilizadas pelas MVS e RNA. Para o treinamento e generalização das MVS, foram utilizadas quatro configurações, formadas a partir de duas funções de erro e duas funções de Kernel. Para configuração, treinamento e generalização das RNA, foi utilizado o software NeuroForest - Volumetric, no qual foram utilizadas configurações de redes do tipo Adaline (Adaptive Linear Element); Multilayer Perceptron (MLP) e Funções de Base Radial (RBF). A qualidade dos ajustes dos modelos de regressão, e das metodologias utilizando RNA e MVS, foram avaliadas utilizando-se o coeficiente de correlação entre os volumes individuais observados e estimados (ryŷ), a raiz quadrada do erro médio, expresso em porcentagem da média (RMSE%), análise gráfica dos resíduos (Res%). Considerando os resultados, MVS e RNA obtiveram desempenho ligeiramente melhores, comparados à metodologia tradicional, nas estimativas de volume individual, demonstrando serem técnicas que se adequaram bem para aplicações na área de mensuração e manejo florestal. Instituto de Ecología, A.C. 2022-03-03 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Artículo evaluado por pares application/pdf text/xml https://myb.ojs.inecol.mx/index.php/myb/article/view/2252 10.21829/myb.2022.2812252 Madera y Bosques; Vol. 28 No. 1 (2022): Spring 2022; e2812252 Madera y Bosques; Vol. 28 Núm. 1 (2022): Primavera 2022; e2812252 2448-7597 1405-0471 spa https://myb.ojs.inecol.mx/index.php/myb/article/view/2252/2346 https://myb.ojs.inecol.mx/index.php/myb/article/view/2252/2387 10.21829/myb.2019.252617 10.21829/myb.2018.241770 http://creativecommons.org/licenses/by-nc-sa/4.0 |
| spellingShingle | Cordeiro, Marcio Assis Arce, Julio Eduardo Guimarães, Fabiane Aparecida Retslaff Bonete, Izabel Passos Silva, Anthoinny Vittória dos Santos de Abreu, Jadson Coelho Binoti, Daniel Henrique Breda Volumetric estimates in eucalyptus stands using support vector machines and artificial neural networks |
| title | Volumetric estimates in eucalyptus stands using support vector machines and artificial neural networks |
| title_full | Volumetric estimates in eucalyptus stands using support vector machines and artificial neural networks |
| title_fullStr | Volumetric estimates in eucalyptus stands using support vector machines and artificial neural networks |
| title_full_unstemmed | Volumetric estimates in eucalyptus stands using support vector machines and artificial neural networks |
| title_short | Volumetric estimates in eucalyptus stands using support vector machines and artificial neural networks |
| title_sort | volumetric estimates in eucalyptus stands using support vector machines and artificial neural networks |
| url | https://myb.ojs.inecol.mx/index.php/myb/article/view/2252 |
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