Stem Taper Estimations With Artificial Neural Networks for Mixed Oriental Beech and Kazdaği Fir Stands in Karabük Region, Turkey

Sakıcı, Oytun Emre; Özdemir, Gülay

doi:10.1590/01047760201824042572

Cited by 27 publications

(11 citation statements)

References 39 publications

(60 reference statements)

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“…Taper equations have been widely used in many inventory studies because they can provide detailed TTV estimation using easily measurable variables such as diameter and height. ANNs worked better than traditional regression methods in many studies, including those that developed taper, volume, diameter, and height equations or estimated leaf area index (Ozçelik et al 2010, 2014, Nunes & Görgens 2016, Sakici & Ozdemir 2018, Ercanli 2020a, 2020b, Socha et al 2020, Senyurt et al 2020, Sandoval & Acuña 2022. However, in many of these studies the learning rates were left at their default values, and overfitting was not taken into account in the ANN model development.…”

Section: Discussionmentioning

confidence: 99%

Analyzing regression models and multi-layer artificial neural network models for estimating taper and tree volume in Crimean pine forests

Sahin

2024

iForest

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The taper and merchantable tree volume equations are the most used models in forestry because of their accuracy in estimating both total and merchantable tree volume. However, numerous studies reported that artificial neural network models show fewer errors and a greater success rate as compared to regression models. This study used data from 200 Crimean pine trees in Turkey's Central Anatolia and Mediterranean Region to assess the performance of artificial neural network (ANN) models and the Max-Burkhart's equation for estimating taper and merchantable tree volume. The most accurate results were obtained using 3 hidden layers and 10 neurons in the taper model and 1 hidden layer and 100 neurons in the volume model. The hyperbolic tangent sigmoid function was used for the ANN analysis and hyper-parameter customization. Using the ANN model with hyper-parameter customization, the AAE in the Max-Burkhart taper model decreased from 9.315 to 6.939 (-25.5%), the RMSE decreased from 3.072 to 2.656 (-13.5%), and the FI increased from 0.964 to 0.966 (+1.23%). Similarly, using the ANN model with hyper-parameter customization, the AAE in the Max-Burkhart volume model decreased from 0.056 to 0.013 (-76.6%), the RMSE decreased from 0.247 to 0.12 (-51.6%), and the FI increased from 0.909 to 0.979 (+7.69%). Our results showed that the ANN models' predictions were more accurate and reliable compared to the Max-Burkhart's equations. We resolved overfitting via hyper-parameter modification, which also allowed for monitoring the impact of error and prediction outputs at various learning rates. It was also possible to develop tree taper and volume equations with lower error rates in both training and validation data, consistent with tree growth trends in both data sets.

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Section: Discussionmentioning

confidence: 99%

Analyzing regression models and multi-layer artificial neural network models for estimating taper and tree volume in Crimean pine forests

Sahin

2024

iForest

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“…Sakici et al, 2008;Crecente-Campo et al, 2009;Burkhart et al, 2019). Of the many model forms, segmented or variable form taper models are often recommended based on taper studies (Berhe and Arnoldsson, 2008;Özcelik and Brooks, 2012;Sakici and Ozdemir, 2018). Rojo et al (2005) and Sakici et al (2008) suggested variable form taper models for maritime pine (Pinus pinaster) in Spain and Bornmullerian fir (Abies nordmanniana subsp.…”

Section: Introductionmentioning

confidence: 99%

“…Simultaneously, segmented and variable form models showed a similar response for Kazdagi fir (Abies nordmanniana subsp. equi-trojani) and Oriental beech (Fagus orientalis) in Turkey (Sakici and Ozdemir, 2018), and white birch (Betula platyphylla) in NE China (Shahzad et al, 2019). Therefore, it is useful to perform a systematic analysis of these taper models so that their application should be extended to other species.…”

Section: Introductionmentioning

confidence: 99%

Segmented taper models form for Manchurian fir and Korean spruce in northeastern China

Hussain

Shahzad

Jiang

et al. 2021

CERNE

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Background: Manchurian fir (Abies nephrolepis Maxim) and Korean spruce (Picea koraiensis Nakai) are primary conifer species of economic and ecological importance in northeast China. Several taper studies have analyzed for fir and spruce species in the countries harboring the boreal forests. However, taper models do not exist for Manchurian fir and Korean spruce in China or abroad. This study aimed to develop stem taper models for these species. A dataset of 188 destructively sampled trees (Manchurian fir 123 and Korean spruce 65) was used to evaluate eight well-known taper models. These models were fitted with generalized non-linear least squares by using 3,570 diameter and height measurements. We incorporated a first-order continuous-time error structure to adjust the inherent autocorrelation. Results:The form-class segmented model of Clark et al. (1991) best predicted the diameter, merchantable volume, and stem volume of the species when the upper stem diameter at 5.3 m was available or predicted. Conclusion:When diameter measurements at 5.3 m were not available, the Kozak (2004) and Max and Burkhart (1976) models were superior to other models in estimating the diameter of both species and volume of Korean spruce. For Manchurian fir, the Fang et al. (2000) model was more accurate in volume estimates.

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“…Alguns estudos vêm demonstrando que as RNA podem ser um método alternativo eficiente nas Ciências Florestais. Como exemplos, as RNA foram aplicadas para obtenção de estimativas de volumes (LEAL, 2015;LACERDA et al, 2017); de biomassa (MONTANO et al, 2017;ÖZÇELIK et al, 2017); de diâmetro à altura do peito (d) a partir de variáveis de sensoriamento remoto ou fatores ambientais (ZHOU et al, 2019); para predições do afilamento do fuste (SCHIKOWSKI et al, 2015;SAKICI & OZDEMIR, 2018); e, até mesmo, para prever as propriedades físicas e mecânicas de madeira termicamente modificada com base na mudança de cor (NASIR et al, 2019).…”

Section: Introductionunclassified

Efeito Do Número De Neurônios Na Camada Oculta Para Relações Hipsométricas De Eucalipto Usando Redes Neurais Artificiais

Bueno

Costa

Cristina

et al. 2020

BIOFIX

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As redes neurais artificiais têm mostrado performance melhor ou pelo menos similar à dos modelos tradicionais de regressão usados a modelagem florestal. No entanto, deve-se atentar para uma configuração que garanta estimativas acuradas e capacidade de generalização, sendo a quantidade de neurônios na camada oculta uma característica crítica a ser decidida. Assim, o presente estudo buscou encontrar a quantidade ótima de neurônios na camada oculta na modelagem da relação hipsométrica em plantio de Eucalyptus saligna Smith. A base de dados foi subdividida de modo aleatório em 50% para treinamento e 50% para validação do modelo, empregando-se a arquitetura de rede neural Multilayer Perceptron (MLP). No processo de modelagem, considerou-se o diâmetro à altura do peito como variável de entrada e a altura como variável de saída. A quantidade de neurônios na camada oculta foi testada com variação entre 1 a 6. Das seis melhores redes treinadas, aquelas que obtiveram neurônios acima de três na camada oculta tenderam a apresentar overfitting quando aplicados aos dados da validação. Com as diferentes arquiteturas na camada oculta, foi possível obter redes treinadas com coeficiente de correlação (r) variando de 0,61 a 0,73 e raiz quadrada do erro quadrático médio de ±1,28 a 1,48 m. Foram escolhidas as RNA com três neurônios na camada oculta devido a sua simplicidade e pouca diferença de acurácia quando comparadas às obtidas com maior número. A quantidade de neurônios a serem considerados na camada oculta deve ser definida com atenção para que o fenômeno modelado conserve capacidade de generalização.

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Stem Taper Estimations With Artificial Neural Networks for Mixed Oriental Beech and Kazdaği Fir Stands in Karabük Region, Turkey

Cited by 27 publications

References 39 publications

Analyzing regression models and multi-layer artificial neural network models for estimating taper and tree volume in Crimean pine forests

Analyzing regression models and multi-layer artificial neural network models for estimating taper and tree volume in Crimean pine forests

Segmented taper models form for Manchurian fir and Korean spruce in northeastern China

Efeito Do Número De Neurônios Na Camada Oculta Para Relações Hipsométricas De Eucalipto Usando Redes Neurais Artificiais

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