Predictive Modeling of Volume and Biomass in Pinus pseudostrobus Using Machine Learning and Allometric Approaches

This study aims to evaluate the effectiveness of machine learning algorithms in predicting key forest metrics-stem volume, root system volume, and organ biomass (including leaves, branches, stem, and root)-for Pinus pseudostrobus var. Lindley, based on morphological measurements from the same trees....

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Vydané v:	Forest science and technology Ročník 21; číslo 1; s. 110 - 122
Hlavní autori:	Antúnez, Pablo, Wehenkel, Christian, Basave-Villalobos, Erickson, Calixto-Valencia, Celi Gloria, Valenzuela-Encinas, César, Ruiz-Aquino, Faustino, Sarmiento-Bustos, David
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Seoul Taylor & Francis 02.01.2025 Taylor & Francis Ltd Taylor & Francis Group 한국산림과학회
Predmet:	Algorithms allometric modeling allometry Biomass Branches Effectiveness Forest management forests heteroskedasticity Learning algorithms Least squares Machine learning machine learning algorithms in forestry Neural networks Pinus pseudostrobus predicting forest biomass Prediction models quantile regression in forest management Quantiles Random Forest algorithm regression analysis root systems Stems Support vector machines technology Trees 임학
ISSN:	2158-0103, 2158-0715, 2158-0715
On-line prístup:	Získať plný text
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