Please use this identifier to cite or link to this item: http://repositorio.ufla.br/jspui/handle/1/49340
Title: Artificial neural networks and regression analysis for volume estimation in native species
Other Titles: Redes neurais artificiais e análise de regressão para estimativa de volume de espécies nativas
Keywords: Native forest
Production volume
Prediction models
Artificial neural networks (ANNs)
Florestas nativas
Volume de produção
Modelos de predição
Redes neurais artificiais (RNAs)
Issue Date: Aug-2021
Publisher: Universidade Federal de Campina Grande
Citation: AMORIM, L. M. et al. Artificial neural networks and regression analysis for volume estimation in native species. Revista Brasileira de Engenharia Agrícola e Ambiental, Campina Grande, v. 25, n. 10, p. 664-669, Oct. 2021. DOI: https://doi.org/10.1590/1807-1929/agriambi.v25n10p664-669 .
Abstract: Modeling is an important tool to estimate forest production in planted areas. Although this issue has been studied worldwide, knowledge regarding volume measurement in specific locations such as Northeast Brazil is still scarce. The present study aimed to evaluated the effectiveness of artificial neural networks (ANNs) and regression analysis in estimating the timber volume of homogeneous stands of Anadantera macrocarpa, Genipa americana, and Mimosa casalpinifolia, in order to better predict the growth and production of these species. Both methods were suitable for estimating the individual volume in 7-year-old stands with different spacing. The Spurr regression model showed better statistical results and dispersion of unbiased errors for Anadantera macrocarpa and Genipa americana, whereas the Shumacher-Hall model provided more accurate volume estimates for Mimosa caesalpinifolia. The ANNs calibrated with two neurons in the middle layer exhibited the best fit for all three species. As such, artificial neural networks can be recommended to estimate the individual volumes of the species analyzed in the study area.
URI: http://repositorio.ufla.br/jspui/handle/1/49340
Appears in Collections:DEG - Artigos publicados em periódicos



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