ABSTRACT

In this study, in order to estimate total tree height, three different model structures with different input variables were produced through the use of 872 tree data points obtained from different development stages and sites in coppice-originated pure sessile oak (Quercus petraea [Matt.] Liebl.) stands. These models were fitted with machine learning techniques such as artificial neural networks (ANNs), decision trees, support vector machines, and random forests. In addition, the model based on DBH was fitted and its parameters were calculated using the ordinary nonlinear least squares method and this model was selected as the best model in Model 1. In other model structures, ANN model was chosen as the best estimation method based on the relative ranking method in which the goodness of fit statistics of the estimation methods were evaluated together. The inclusion of stand variables in addition to the DBH measurement in the model increased the R² by about 36% and reduced the error rate by 55%.

摘要

在这项研究中，为了估计总树高，通过使用从源自矮林的纯无梗橡树（ Quercus petraea[Matt.] Liebl.) 站立。这些模型配备了机器学习技术，例如人工神经网络 (ANN)、决策树、支持向量机和随机森林。此外，对基于DBH的模型进行拟合，并采用普通非线性最小二乘法计算其参数，并在模型1中选择该模型作为最佳模型。在其他模型结构中，选择ANN模型作为基于关于相对排序法，其中估计方法的拟合优度统计一起评估。除了模型中的 DBH 测量外，还包括标准变量，使R ²增加了约 36%，并将错误率降低了 55%。