Abstract Here, we present one of the first attempts to use a machine learning model for the prediction and interpretation of tree basal area increment (BAI) based on data from the National Forest Inventory (NFI). The developed model is based on the random forest (RF) algorithm, trained with 18 independent variables and 15,580 data points (trees from the Slovenian NFI). The RF model was trained for four individual species and two groups of species and evaluated using 10-fold blocked cross-validation. Squared correlation coefficients calculated for independent data ranged from 0.289 for Scots pine (Pinus Sylvestris) to 0.342 for maple and ash species (Acer sp. and Fraxinus sp.), 0.429 for oak species (Quercus sp.), 0.475 for Norway spruce (Picea abies), 0.486 for common beech (Fagus sylvatica), and 0.565 for silver fir (Abies alba). The most important predictor variables were the basal areas of individual trees and their competition status, expressed as the basal area in larger trees and tree social position. Simulations of selected key variables revealed different ecological traits of the studied species: silver fir and Norway spruce have the highest growth characteristics, while common beech has the strongest competition potential. For valuable broadleaves and silver fir, site specific conditions play an important role in tree growth, while oaks and Scots pine have less site-specific demands and wider ecological amplitudes. Finally, in comparison to BAI models from similar studies, the presented RF model showed similar accuracy and could potentially be used as a tool in forest management practices and for making professionally informed decisions.

中文翻译：

---

基于国家森林清查数据的基础面积增量预测的随机森林模型

摘要 在这里，我们首次尝试使用机器学习模型根据国家森林清单 (NFI) 的数据预测和解释树基面积增量 (BAI)。开发的模型基于随机森林 (RF) 算法，使用 18 个自变量和 15,580 个数据点（来自斯洛文尼亚 NFI 的树）进行训练。RF 模型针对四个单独的物种和两组物种进行了训练，并使用 10 倍阻塞交叉验证进行了评估。为独立数据计算的平方相关系数范围从苏格兰松 (Pinus Sylvestris) 的 0.289 到枫树和白蜡树种 (Acer sp. 和 Fraxinus sp.) 的 0.342，橡树种 (Quercus sp.) 的 0.429，挪威云杉 (Picea) 的 0.475 abies)，普通山毛榉 (Fagus sylvatica) 为 0.486，银杉 (Abies alba) 为 0.565。最重要的预测变量是个体树木的基面积及其竞争状态，表示为较大树木的基面积和树木的社会地位。对选定关键变量的模拟揭示了所研究物种的不同生态特征：银杉和挪威云杉的生长特征最高，而普通山毛榉的竞争潜力最强。对于有价值的阔叶树和银杉，特定地点的条件在树木生长中起着重要作用，而橡树和苏格兰松树的特定地点需求较少，生态范围更广。最后，与来自类似研究的 BAI 模型相比，所提出的 RF 模型显示出相似的准确性，并且有可能用作森林管理实践和做出专业明智决策的工具。