Analyzing a Combination of Factors for Thinning Trees with a Neural Network

概要

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A neural network, which is a computer model applied to an artificially simulated process of neurons, has various advantages. For example, it allows treating both quantitative and qualitative data at the same time. In a previous paper, the author suggested that a neural network, was an effective method to analyze subjective forest information such as the selection of trees for thinning. The purpose of this paper is to analyze the relationship between a combination of factors and degrees of contribution to a neural network structure. The AIC (Akaike's information criterion) was used as an information criterion for model selection and a three-layer back-propagation model was used as the learning algorithm. It was found that : (1) qualitative factors (i. e., crown volume, stem quality and "defects") conferred more accurate estimation results than quantitative factors (DBH and height), and (2) "defects" was the most important factor, especially if the number of output units was two, because it was possible to discriminate sufficiently even in the absence of other factors. It is important to consider a combination of factors in the model when applying a neural network ; Addtionally, few factors usually result in more useful and meaningful models.
日本森林学会の論文
2001-05-16