Recursive diameter prediction for calculating merchantable volume of Eucalyptus clones without previous knowledge of total tree height using artificial neural networks

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• 作者：Fabrí ; zzio Alphonsus A.M.N. Soares^a ; ^{fabrizzio@inf.ufg.br} ; Edna Lú ; cia Flô ; res^b ; Christian Dias Cabacinha^c ; Gilberto Arantes Carrijo^b ; Antô ; nio Clá ; udio Paschoarelli Veiga^b
• 关键词：Multilayer perceptron ; Radial basis function ; Recursive prediction ; Forest inventory ; Estimated diameters ; Volumetric equations
• 刊名：Applied Soft Computing Journal
• 出版年：2012
• 期刊代码：9_15684946
• 类别：cp
• 出版时间：August, 2012
• 卷：12
• 期：8
• 页码：2030-2039
• 文件大小：521 K

摘要

In this work, diameters of Eucalyptus trees are predicted by means of Multilayer Perceptron and Radial Basis Function artificial neural networks. By taking only three diameter measures at the base of the tree, diameters are predicted recursively until they reach the value of minimum merchantable diameter, with no previous knowledge of total tree height. It was considered the diameter top of 4 cm outside bark as minimum merchantable diameter. The training was conducted with only 10%of the trees from the total planted site. The Smalian method utilizes the predicted diameters to calculate merchantable tree volumes. The performance of the proposed model was satisfactory when predicted diameters and volumes are compared to actual ones.