Survival analysis is composed of a group of analytical approaches that can be used to predict the occurrence of harvest activities, which provides insightful information about the dynamics of natural resources utilization in a region. Recently, random survival forest (RSF) has been proposed in survival analysis to capture the complex relationships among variables. The main objective of this study was to employ the RSF algorithm to examine the temporal evolution of tree harvest, accounting for stand and environmental variables. Specifically, the predictability of the RSF model was compared with the Cox proportional hazard (Cox) model, a popular model in survival analysis. Important variables in explaining the variation of harvest time were identified. Data collected by the USDA Forest Service, Forest Inventory and Analysis (FIA) program from permanent plots in the southern Appalachian region were utilized in the analysis. Results showed that the RSF model consistently outperformed the Cox model based on prediction accuracy. Among 14 variables examined, ownership, forest type, elevation, state, and slope emerged as most important. Utilizing only these five variables in a reduced model produced satisfactory prediction accuracy compared to the full model (i.e., the models with all variables included). The findings of this work provide insights for forest managers and policy makers to utilize survival analysis methods in understanding harvest activities at the regional scale.

中文翻译：

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使用随机生存森林算法预测混种森林的采伐时间


生存分析由一组分析方法组成，可用于预测收获活动的发生，从而提供有关一个地区自然资源利用动态的有见地信息。最近，在生存分析中提出了随机生存森林 （RSF） 来捕捉变量之间的复杂关系。本研究的主要目的是采用 RSF 算法来检查树木采伐的时间演变，考虑林分和环境变量。具体来说，将 RSF 模型的可预测性与 Cox 比例风险 （Cox） 模型进行了比较，后者是生存分析中的一种常用模型。确定了解释收获时间变化的重要变量。分析使用了美国农业部林务局、森林清查和分析 （FIA） 计划从阿巴拉契亚南部地区的永久地块收集的数据。结果表明，基于预测准确性，RSF 模型始终优于 Cox 模型。在检查的 14 个变量中，所有权、森林类型、海拔、状态和坡度是最重要的。与完整模型（即包含所有变量的模型）相比，在简化模型中仅使用这五个变量产生了令人满意的预测准确性。这项工作的结果为森林管理者和政策制定者提供了见解，以利用生存分析方法来了解区域尺度的采伐活动。