Abstract Key message We modeled 10-year net stand volume growth with four machine learning (ML) methods, i.e., artificial neural networks (ANN), support vector machines (SVM), random forests (RF), and nearest neighbor analysis (NN), and with linear regression analysis. Incorporating interactions of multiple variables, the ML methods ANN and SVM predicted nonlinear system behavior and unraveled complex relations with greater accuracy than regression analysis. Context Investigating the quantitative and qualitative characteristics of short-term forest dynamics is essential for testing whether the desired goals in forest-ecosystem conservation and restoration are achieved. Inventory data from the Jojadeh section of the Farim Forest located in the uneven-aged, mixed Hyrcanian Forest were used to model and predict 10-year net annual stand volume increment with new machine learning technologies. Aims The main objective of this study was to predict net annual stand volume increment as the preeminent factor of forest growth and yield models. Methods In the current study, volume increment was modeled from two consecutive inventories in 2003 and 2013 using four machine learning techniques that used physiographic data of the forest as input for model development: (i) artificial neural networks (ANN), (ii) support vector machines (SVM), (iii) random forests (RF), and (iv) nearest neighbor analysis (NN). Results from the various machine learning technologies were compared against results produced with regression analysis. Results ANNs and SVMs with a linear kernel function that incorporated field-measurements of terrain slope and aspect as input variables were able to predict plot-level volume increment with a greater accuracy (94%) than regression analysis (87%). Conclusion These results provide compelling evidence for the added utility of machine learning technologies for modeling plot-level volume increment in the context of forest dynamics and management.

中文翻译：

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由机器学习方法开发的地块级体积增量模型分析应用于不均匀年龄的混交林

摘要 关键信息 我们使用四种机器学习 (ML) 方法，即人工神经网络 (ANN)、支持向量机 (SVM)、随机森林 (RF) 和最近邻分析 (NN) 来模拟 10 年的净站量增长, 并进行线性回归分析。结合多个变量的相互作用，ML 方法 ANN 和 SVM 预测非线性系统行为并比回归分析更准确地解开复杂关系。背景调查短期森林动态的定量和定性特征对于测试森林生态系统保护和恢复的预期目标是否实现至关重要。来自位于不均匀年龄的 Farim 森林 Jojadeh 部分的库存数据，混合海尔卡尼亚森林被用来通过新的机器学习技术对 10 年净林量增量进行建模和预测。目的 本研究的主要目的是预测每年林分净增量作为森林生长和产量模型的主要因素。方法 在当前的研究中，使用四种机器学习技术从 2003 年和 2013 年的两个连续清单中模拟了体积增量，这些技术使用森林的自然地理数据作为模型开发的输入：（i）人工神经网络（ANN），（ii）支持向量机 (SVM)、(iii) 随机森林 (RF) 和 (iv) 最近邻分析 (NN)。将各种机器学习技术的结果与回归分析产生的结果进行比较。结果 具有线性核函数的 ANN 和 SVM 将地形坡度和坡向的现场测量值作为输入变量，能够以比回归分析 (87%) 更高的准确度 (94%) 预测地块级体积增量。结论这些结果为机器学习技术在森林动态和管理背景下模拟地块级体积增量的附加效用提供了令人信服的证据。