Modelagem da distribuição de diâmetros utilizando autômatos celulares e redes neurais artificiais

Binoti, Daniel Henrique Breda; Binoti, Mayra Luiza Marques da Silva; Leite, Hélio Garcia; Silva, Antonilmar Araújo Lopes da; Albuquerque, Ana Carolina Rattacaso Marino de Mattos

doi:10.1590/s0104-77602013000400019

Cited by 7 publications

(9 citation statements)

References 19 publications

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“…This type of technique, especially the Artificial Neural Networks (ANN), has been applied in several studies in forestry (e.g. BINOTI et al, 2013;CASTRO et al, 2013;BINOTI et al, 2015;DIAMANTOPOULOU et al, 2015;REIS et al, 2016). This methods allows to extract information from a massive database consisting of continuous and categorical variables in order to recognize patterns between the input variables and a variable of interest.…”

Section: Introductionmentioning

confidence: 99%

Site Classification for Eucalypt Stands Using Artificial Neural Network Based on Environmental and Management Features

Cosenza

Soares

Alcântara³

et al. 2017

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Several methods have been proposed to perform site classification for timber production. However, there is frequent need to assess site productive capacity before forest establishment. This has motivated the application of Artificial Neural Networks (ANN) for site classification. Hereby, the traditional guide curve (GC) procedure was compared to the ANN with no stand measures as input. In addition, different ANN settings were tested to assess the best setting. The variables used to train the ANN were: climatic variables, soil types, spacing and genetic material. The results from the ANN and the GC methods were compared to the observed classes, which were defined using the observed dominant high at the age of seven years. The comparison was performed using the Kappa coefficient (K) and descriptive analysis. The results showed that the cost function “Cross Entropy” and the output activation function “Softmax” were the best for this purpose. The ANN classification resulted in substantial agreement with the observed indices against a moderate agreement of the GC procedure. The change in growth patterns throughout the rotation may have hindered the proper classification by the CG method, which does not happen with the ANN. Moreover, the GC method shows efficiency on classification in cases which data from stands at the age close to the reference age are available. Also, it could be possible to improve its accuracy if another advanced regression techniques were applied. However, the ANN method presented here is not sensible to growth instability and allows classifying sites with no plantation history.

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Section: Introductionmentioning

confidence: 99%

Site Classification for Eucalypt Stands Using Artificial Neural Network Based on Environmental and Management Features

Cosenza

Soares

Alcântara³

et al. 2017

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“…Esse resultado demonstrou que o ajustamento foi realizado com precisão e corrobora com os resultados obtidos por Binoti et al (2013) Para exemplificar a transição utilizando RNA e AC, foi escolhida aleatoriamente uma parcela (parcela permanente 10) dos dados da validação (Tabela 3). A classe 32,5 cm no ano t = 0 (1981), por exemplo, apresentava probabilidade de ocorrência de árvores de 0,01; as classes vizinhas tinham probabilidade de 0,04, 0,03, 0,02 e 0,02 para 22,5, 27,5, 37,5 e 42,5 cm, respectivamente.…”

Section: Resultsunclassified

“…A umidade relativa média é de 86% e a precipitação média anual é de 2.100 mm. Apresenta topografia plana a ondulada, com ocorrência de solo tipo Latossolo Amarelo Distrófico (COSTA FILHO et al, 1980 1982, 1983, 1985, 1987, 1992, 1997, 2007, 2010 e 2012. Autômato celular e Redes Neurais Artificiais Para um modelo unidimensional de AC, o valor i da célula no tempo t, denominado ci(t), evolui de acordo com a regra F, que é função do ci(t) e das outras células que se encontram dentro da extensão r (para ambas as direções) de ci(t) (ILACHINSKI, 2001;BINOTI et al, 2013), conforme a equação (1).…”

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Prognose Da Distribuição Diamétrica Na Amazônia Utilizando Redes Neurais Artificiais E Autômatos Celulares

Reis

Souza

Reis

et al. 2018

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ResumoA análise da distribuição diamétrica é imprescindível para o manejo florestal, pois subsidia a avaliação dos estoques remanescentes e de colheita de madeira. Este trabalho teve como objetivo projetar a distribuição diamétrica utilizando redes neurais artificias (RNA) e autômatos celulares (AC). Em 1979, foi realizada colheita seletiva com intensidade de 72,5 m 3 ha -1 em uma área de 64 ha na Floresta Nacional do Tapajós -PA. Na projeção da distribuição, foram empregadas RNA como regra de evolução de AC, considerando a probabilidade de árvores da projeção e suas vizinhas mais próximas. A projeção em todos os períodos analisados não apresentou diferença estatística a 5% de significância em relação à observada, demonstrando que a projeção seguiu a tendência da dinâmica da distribuição. As RNA e AC são eficientes e podem ser utilizadas para projetar a distribuição diamétrica ao longo do ciclo de corte. Palavras-Chave: Inteligência artificial, manejo florestal, modelagem. Abstract Prognosis of the diameter distribution in the Amazon by using artificial neural networks and cellular automata.The analysis of the diameter distribution is indispensable for forest management since it subsidizes the assessment of the remaining stocks and of wood harvest. This work aimed to project the diameter distribution by using artificial neural networks (ANN) and cellular automata (CA). In 1979, selective harvest was performed with an intensity of 72.5 m3 ha-1 in an area of 64 ha in the Floresta Nacional do Tapajós (state of Pará, Brazil). In the distribution projection, ANNs were used as a rule for CA evolution, considering the probability of projection's trees and their closest neighbors. The projection from all the analyzed periods showed no statistical difference at 5% of significance in relation to the observed one, which demonstrates that the projection followed the tendency of the distribution's dynamics. ANN and CA are efficient and can be used to design the diameter distribution during the cutting cycle.

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“…In forest management ANN's have also commonly been applied to estimate tree volume (Gorgens et al 2009, Silva et al 2009, Diamantopoulou and Milios 2010, Özçelik et al 2010, Yu & Jia-yin 2012, Özçelik et al 2014, growth modelling (Castro et al 2013), tree height (Binoti et al 2013a), or to describe diameter distribution (Leite et al 2011;Binoti et al 2013b). However, none of these applications ; ii) to incorporate forest cover as a forest management-derived variable into the selected modelling approach and to evaluate its predictive ability; iii) to study the model performance when soil moisture needs to be derived from other modelling approaches (Gotilwa+).…”

Section: Introductionmentioning

confidence: 99%

Water-oriented management in forest plantations: combining hydrology, dendrochronology and ecophysiology

Fernandes¹,

Jose²

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Modelagem da distribuição de diâmetros utilizando autômatos celulares e redes neurais artificiais

Cited by 7 publications

References 19 publications

Site Classification for Eucalypt Stands Using Artificial Neural Network Based on Environmental and Management Features

Site Classification for Eucalypt Stands Using Artificial Neural Network Based on Environmental and Management Features

Prognose Da Distribuição Diamétrica Na Amazônia Utilizando Redes Neurais Artificiais E Autômatos Celulares

Water-oriented management in forest plantations: combining hydrology, dendrochronology and ecophysiology

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