Deep Learning Algorithms (DLA) have become prominent as an application of Artificial Intelligence (AI) Techniques since 2010. This paper introduces the DLA to predict the relationships between individual tree height (ITH) and the diameter at breast height (DBH). A set of 2024 pairs of individual height and diameter at breast height measurements, originating from 150 sample plots located in stands of even aged and pure Anatolian Crimean Pine (Pinus nigra J.F. Arnold ssp. pallasiana (Lamb.) Holmboe) in Konya Forest Enterprise. The present study primarily investigated the capability and usability of DLA models for predicting the relationships between the ITH and the DBH sampled from some stands with different growth structures. The 80 different DLA models, which involve different the alternatives for the numbers of hidden layers and neuron, have been trained and compared to determine optimum and best predictive DLAs network structure. It was determined that the DLA model with 9 layers and 100 neurons has been the best predictive network model compared as those by other different DLA, Artificial Neural Network, Nonlinear Regression and Nonlinear Mixed Effect models. The alternative of 100 # neurons and 9 # hidden layers in deep learning algorithms resulted in best predictive ITH values with root mean squared error (RMSE, 0.5575), percent of the root mean squared error (RMSE%, 4.9504%), Akaike information criterion (AIC, − 998.9540), Bayesian information criterion (BIC, 884.6591), fit index (FI, 0.9436), average absolute error (AAE, 0.4077), maximum absolute error (max. AE, 2.5106), Bias (0.0057) and percent Bias (Bias%, 0.0502%). In addition, these predictive results with DLAs were further validated by the Equivalence tests that showed the DLA models successfully predicted the tree height in the independent dataset. This study has emphasized the capability of the DLA models, novel artificial intelligence technique, for predicting the relationships between individual tree height and the diameter at breast height that can be required information for the management of forests.

中文翻译：

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创新的深度学习人工智能应用程序，用于预测个体树高和胸高直径之间的关系

自2010年以来，作为人工智能（AI）技术的一种应用，深度学习算法（DLA）变得十分突出。本文介绍了DLA来预测单个树高（ITH）与胸高直径（DBH）之间的关系。一组2024对乳房高度测量值的个体高度和直径对，来自位于科尼亚森林企业的均匀老龄和纯净的安纳托利亚克里米亚松（Pinus nigra JF Arnold ssp。pallasiana（Lamb。）Holmboe）林分中的150个样地。本研究主要研究了DLA模型预测ITH和DBH之间关系的能力和可用性，这些林分采自一些生长结构不同的林分。80种不同的DLA模型，涉及隐藏层和神经元数量的不同选择，已经过培训和比较以确定最佳和最佳预测DLA网络结构。与其他不同的DLA模型，人工神经网络模型，非线性回归模型和非线性混合效应模型相比，具有9层100个神经元的DLA模型已被确定为最佳的预测网络模型。深度学习算法中100＃神经元和9＃隐藏层的替代产生了最佳预测ITH值，具有均方根误差（RMSE，0.5575），均方根误差百分比（RMSE％，4.9504％），Akaike信息标准（AIC，− 998.9540），贝叶斯信息标准（BIC，884.6591），拟合指数（FI，0.9436），平均绝对误差（AAE，0.4077），最大绝对误差（最大AE，2.5106），偏差（0.0057）和百分比偏见（Bias％，0.0502％）。此外，这些等效的DLA预测结果通过等效测试进一步验证，该测试显示DLA模型成功预测了独立数据集中的树高。这项研究强调了DLA模型（新颖的人工智能技术）在预测个体树高与胸高直径之间的关系（这些信息可能是管理森林的信息）时的能力。