ABSTRACT Modern forest harvesters automatically collect large amounts of standardized work-related data. Statistical machine learning methods enable detailed analyses of large databases from wood harvesting operations. In the present study, gradient boosted machine (GBM), support vector machine (SVM) and ordinary least square (OLS) regression were implemented and compared in predicting the productivity of cut-to-length (CTL) harvesting based on operational monitoring files generated by the harvesters’ on-board computers. The data consisted of 1,381 observations from 27 operators and 19 single-grip harvesters. Each tested method detected the mean stem volume as the most significant factor affecting productivity. Depending on the modeling approach, 33–59% of variation was due to the operators. The best GBM model was able to predict the productivity with 90.2% R2, whereas OLS and the SVM machine reached R 2-values of 89.3% and 87% R2, respectively. OLS regression still proved to be an effective method for predicting productivity of CTL harvesting with a limited number of observations and variables, but more powerful GBM and SVM show great potential as the amount of data increases along with the development of various big data applications.

中文翻译：

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使用统计机器学习方法模拟机械化 CTL 收获的生产力

摘要 现代森林采伐机自动收集大量标准化的工作相关数据。统计机器学习方法可以对木材采伐操作中的大型数据库进行详细分析。在本研究中，梯度提升机 (GBM)、支持向量机 (SVM) 和普通最小二乘法 (OLS) 回归在预测定长切割 (CTL) 收获的生产力方面进行了实施和比较，基于生成的操作监控文件通过收割机的机载计算机。数据包括来自 27 名操作员和 19 台单手柄收割机的 1,381 次观察结果。每种测试方法都检测到平均茎体积是影响生产力的最重要因素。根据建模方法，33-59% 的变化是由操作员造成的。最好的 GBM 模型能够以 90.2% 的 R2 预测生产率，而 OLS 和 SVM 机器分别达到了 89.3% 和 87% R2 的 R 2 值。OLS 回归仍然被证明是一种在有限数量的观察和变量下预测 CTL 收获生产力的有效方法，但随着各种大数据应用的发展，随着数据量的增加，更强大的 GBM 和 SVM 显示出巨大的潜力。