ARTIFICIAL NEURAL NETWORKS AND MIXED-EFFECTS MODELING TO DESCRIBE THE STEM PROFILE OF Pinus taeda L.

Bonete, Izabel Passos; Arce, Júlio Eduardo; Filho, Afonso Figueiredo; Retslaff, Fabiane Aparecida de Souza; Lanssanova, Luciano Rodrigo

doi:10.5380/rf.v50i1.61764

Cited by 5 publications

(8 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Ela é uma expressão em que o volume da madeira é apresentado como função de outras grandezas ou variáveis da árvore (normalmente o diâmetro a altura do peito e a altura) que podem ser medidas e estimadas por meio não destrutivo (Batista et al, 2014). Porém nos últimos anos, as equações volumétricas têm sido utilizadas em estudos comparativos juntamente com aplicações de ferramentas da inteligência artificial, haja vista os bons resultados obtidos em alguns trabalhos na ciência florestal, dada à sua flexibilidade no treinamento e modelagem das relações entre variáveis, capacidade de aprendizado de informações de um conjunto de dados e a generalização desse aprendizado para dados desconhecidos (Binoti et al 2016;Bonete et al, 2019;Abreu et al, 2020).…”

Section: Introductionunclassified

Estimativas volumétricas em povoamentos de eucalipto utilizando máquinas de vetores de suporte e redes neurais artificiais

Cordeiro¹,

Arce²,

Retslaff³

et al. 2022

MYB

Self Cite

View full text Add to dashboard Cite

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. PALAVRAS CHAVE: análise de regressão, aprendizado de máquina, volumetria.

show abstract

Section: Introductionunclassified

Estimativas volumétricas em povoamentos de eucalipto utilizando máquinas de vetores de suporte e redes neurais artificiais

Cordeiro¹,

Arce²,

Retslaff³

et al. 2022

MYB

Self Cite

View full text Add to dashboard Cite

show abstract

“…Machine learning algorithms are increasingly being used in species distribution and ecological niche modeling (Prasad et al 2006;Cutler et al 2007;Hannemann et al 2015;Liang et al 2016;Prasad 2018;Gobeyn et al 2019), forest resources (Stojanova et al 2010;Görgens et al 2015) and climate change studies (Thuille 2003;Bastin et al 2019), among others. To determine to what extent these "new" methodologies can contribute to improving our understanding and prediction capacity within the field of environmental sciences, comparative studies are required between those models that have been used historically and those fed by artificial intelligence algorithms (Özçelik et al 2013;Diamantopoulou et al 2015;Hill et al 2017;Bonete et al 2020). Yet, many machine learning algorithms have been developed in recent years, and each of them may be more or less appropriate depending on the specific tasks and research objectives (Thessen 2016).…”

Section: Introductionmentioning

confidence: 99%

“…This highlights the need for systematic studies allowing for discerning the most suitable methodology according to a given research objective and data. Although several studies have analysed the performance of different analytical approaches (Hill et al 2017;Bonete et al 2020;Mayfield et al 2020), existing ecological research addressing systematic assessments and comparisons of alternative modeling and predictive methods is scarce, making it difficult to provide clear methodological recommendations about the suitability of different approaches. Besides, in the field of environmental sciences, often, extrapolations in biophysically differentiated areas are required, which makes it necessary to take even more into account the data spatial dependencies.…”

Section: Introductionmentioning

confidence: 99%

Performance of statistical and machine learning-based methods for predicting biogeographical patterns of fungal productivity in forest ecosystems

et al. 2021

View full text Add to dashboard Cite

Background The prediction of biogeographical patterns from a large number of driving factors with complex interactions, correlations and non-linear dependences require advanced analytical methods and modeling tools. This study compares different statistical and machine learning-based models for predicting fungal productivity biogeographical patterns as a case study for the thorough assessment of the performance of alternative modeling approaches to provide accurate and ecologically-consistent predictions. Methods We evaluated and compared the performance of two statistical modeling techniques, namely, generalized linear mixed models and geographically weighted regression, and four techniques based on different machine learning algorithms, namely, random forest, extreme gradient boosting, support vector machine and artificial neural network to predict fungal productivity. Model evaluation was conducted using a systematic methodology combining random, spatial and environmental blocking together with the assessment of the ecological consistency of spatially-explicit model predictions according to scientific knowledge. Results Fungal productivity predictions were sensitive to the modeling approach and the number of predictors used. Moreover, the importance assigned to different predictors varied between machine learning modeling approaches. Decision tree-based models increased prediction accuracy by more than 10% compared to other machine learning approaches, and by more than 20% compared to statistical models, and resulted in higher ecological consistence of the predicted biogeographical patterns of fungal productivity. Conclusions Decision tree-based models were the best approach for prediction both in sampling-like environments as well as in extrapolation beyond the spatial and climatic range of the modeling data. In this study, we show that proper variable selection is crucial to create robust models for extrapolation in biophysically differentiated areas. This allows for reducing the dimensions of the ecosystem space described by the predictors of the models, resulting in higher similarity between the modeling data and the environmental conditions over the whole study area. When dealing with spatial-temporal data in the analysis of biogeographical patterns, environmental blocking is postulated as a highly informative technique to be used in cross-validation to assess the prediction error over larger scales.

show abstract

“…Machine learning algorithms are increasingly being used in species distribution and ecological niche modelling (Prasad et al, 2006;Culter et al, 2007;Hannemann et al, 2015;Liang et al 2016;Prasad, 2018;Gobeyn et al, 2019), forest resources (Stojanova et al, 2010;Görgens et al, 2015) and climate change studies (Thuille, 2003;Bastin et al, 2019), among others. To determine to what extent these "new" methodologies can contribute to improving our understanding and prediction capacity within the field of environmental sciences, comparative studies are required between those models that have been used historically and those fed by artificial intelligence algorithms (Özçelik et al, 2013;Diamantopoulou et al, 2015;Hill et al, 2016;Bonete et al, 2020). Yet, many machine learning algorithms have been developed in recent years, and each of them may be more or less appropriate depending on the specific tasks and research objectives (Thessen, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…This highlights the need for systematic studies allowing for discerning the most suitable methodology according to a given research objective and data. Although several studies have analysed the performance of different analytical approaches (Hill et al, 2016;Bonete et al, 2020;Mayfield et al, 2020), existing ecological research addressing systematic assessments and comparisons of alternative modelling and predictive methods is scarce, making it difficult to provide clear methodological recommendations about the suitability of different approaches. Besides, in the field of environmental sciences, often, extrapolations in biophysically differentiated areas are required, which makes it necessary to take even more into account the data spatial dependencies.…”

Section: Introductionmentioning

confidence: 99%

Performance of Statistical and Machine Learning-Based Methods for Predicting Biogeographical Patterns of Fungal Productivity in Forest Ecosystems

Morera

Aragón

Bonet

et al. 2020

Preprint

View full text Add to dashboard Cite

BackgroundThe prediction of biogeographical patterns from a large number of driving factors with complex interactions, correlations and non-linear dependences require advanced analytical methods and modelling tools. This study compares different statistical and machine learning models for predicting fungal productivity biogeographical patterns as a case study for the thorough assessment of the performance of alternative modelling approaches to provide accurate and ecologically-consistent predictions.MethodsWe evaluated and compared the performance of two statistical modelling techniques, namely, generalized linear mixed models and geographically weighted regression, and four machine learning models, namely, random forest, extreme gradient boosting, support vector machine and deep learning to predict fungal productivity. We used a systematic methodology based on substitution, random, spatial and climatic blocking combined with principal component analysis, together with an evaluation of the ecological consistency of spatially-explicit model predictions.ResultsFungal productivity predictions were sensitive to the modelling approach and complexity. Moreover, the importance assigned to different predictors varied between machine learning modelling approaches. Decision tree-based models increased prediction accuracy by ~7% compared to other machine learning approaches and by more than 25% compared to statistical ones, and resulted in higher ecological consistence at the landscape level.ConclusionsWhereas a large number of predictors are often used in machine learning algorithms, in this study we show that proper variable selection is crucial to create robust models for extrapolation in biophysically differentiated areas. When dealing with spatial-temporal data in the analysis of biogeographical patterns, climatic blocking is postulated as a highly informative technique to be used in cross-validation to assess the prediction error over larger scales. Random forest was the best approach for prediction both in sampling-like environments as well as in extrapolation beyond the spatial and climatic range of the modelling data.

show abstract

ARTIFICIAL NEURAL NETWORKS AND MIXED-EFFECTS MODELING TO DESCRIBE THE STEM PROFILE OF Pinus taeda L.

Cited by 5 publications

References 0 publications

Estimativas volumétricas em povoamentos de eucalipto utilizando máquinas de vetores de suporte e redes neurais artificiais

Estimativas volumétricas em povoamentos de eucalipto utilizando máquinas de vetores de suporte e redes neurais artificiais

Performance of statistical and machine learning-based methods for predicting biogeographical patterns of fungal productivity in forest ecosystems

Performance of Statistical and Machine Learning-Based Methods for Predicting Biogeographical Patterns of Fungal Productivity in Forest Ecosystems

Contact Info

Product

Resources

About