Machine Learning Methods for Woody Volume Prediction in Eucalyptus

Dthenifer Cordeiro Santana, Regimar Garcia dos Santos, Pedro Henrique Neves da Silva, Hemerson Pistori, Larissa Pereira Ribeiro Teodoro, Nerison Luis Poersch, Gileno Brito de Azevedo, Glauce Taís de Oliveira Sousa Azevedo, Carlos Antonio da Silva Junior () and Paulo Eduardo Teodoro
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Dthenifer Cordeiro Santana: Department of Agronomy, State University of São Paulo (UNESP), Ilha Solteira 15385-000, SP, Brazil
Regimar Garcia dos Santos: Department of Agronomy, State University of São Paulo (UNESP), Ilha Solteira 15385-000, SP, Brazil
Pedro Henrique Neves da Silva: Faculty of Computing, Federal University of Mato Grosso do Sul (UFMS), Campo Grande 79070-900, MS, Brazil
Hemerson Pistori: Faculty of Computing, Federal University of Mato Grosso do Sul (UFMS), Campo Grande 79070-900, MS, Brazil
Larissa Pereira Ribeiro Teodoro: Campus de Chapadão do Sul, Federal University of Mato Grosso do Sul (UFMS), Chapadão do Sul 79560-000, MS, Brazil
Nerison Luis Poersch: Department of Agronomy, Federal University of Fronteira do Sul (UFFS), Cerro Largo 97900-000, RS, Brazil
Gileno Brito de Azevedo: Campus de Chapadão do Sul, Federal University of Mato Grosso do Sul (UFMS), Chapadão do Sul 79560-000, MS, Brazil
Glauce Taís de Oliveira Sousa Azevedo: Campus de Chapadão do Sul, Federal University of Mato Grosso do Sul (UFMS), Chapadão do Sul 79560-000, MS, Brazil
Carlos Antonio da Silva Junior: Department of Geography, State University of Mato Grosso (UNEMAT), Sinop 78555-000, MT, Brazil
Paulo Eduardo Teodoro: Campus de Chapadão do Sul, Federal University of Mato Grosso do Sul (UFMS), Chapadão do Sul 79560-000, MS, Brazil

Sustainability, 2023, vol. 15, issue 14, 1-11

Abstract: Machine learning (ML) algorithms can be used to predict wood volume in a faster and more accurate way, providing reliable answers in forest inventories. The objective of this work was to evaluate the performance of different ML techniques to predict the volume of eucalyptus wood, using diameter at breast height (DBH) and total height (Ht) as input variables, obtained by measuring DBH and Ht of 72 trees of six eucalyptus species ( Eucalyptus camaldulensis , E. uroplylla , E. saligna , E. grandis , E. urograndis , and Corymbria citriodora ). The trees were cut down in two different epochs, rendering 48 samples at 24 months and 24 samples at 48 months, and the volume of each tree was measured using the Smailian method. This research explores five machine learning models, namely artificial neural networks (ANN), K-nearest neighbor (KNN), multiple linear regression (LR), random forest (RF) and support vector machine (SVM), to estimate the volume of eucalyptus wood using DBH and Ht. Artificial neural networks achieved higher correlations between observed and estimated wood volume values. However, the RF outperformed all models by providing lower MAE and higher correlations between observed and estimated wood volume values. Therefore, RF is the most accurate for predicting wood volume in eucalyptus species.

Keywords: tree volume; forestry inventory; shallow learner (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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