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Comparison of machine learning methods for dry biomass estimation based on green logging residues chips
International Journal of Forest Engineering ( IF 1.9 ) Pub Date : 2021-03-14 , DOI: 10.1080/14942119.2021.1892415
Rodrigo De la Fuente 1 , Jorge Cancino 2 , Eduardo Acuña 2
Affiliation  

ABSTRACT

This work shows how modern machine learning techniques can be used to solve current problems faced by the forestry industry. More specifically, the focus is on comparing the predictive performance of several algorithms on estimating the dry weight, in tons, of chip residues. The dataset contains samples obtained during 22 months from 220 trucks coming from 17 different farms located within the area spanned by the Biobío and Maule regions, Chile. Once the trucks arrived, samples were collected and dried to compute the dry tons carried by each truck, which was set as the dependent variable. Using open-source software implementations of state-of-the-art algorithms it was possible to determine, for our data, that even though the non-parametric models Gradient Boosting Machines (GBM) and Neural Networks (NNET) outperformed the linear regression (LM) model, they are not statistically superior to the LASSO regression (GLMNET), an improved version of the LM model. Additionally, it was observed that seasonality affects the ratio of green tons to dry tons a truck can deliver to a power plant during the year. Finally, the continuous variables green tons, elevation, east and north (longitude-latitude) also contribute to explaining the dependent variable.



中文翻译:

基于绿色采伐残留芯片的干生物量估算机器学习方法比较

摘要

这项工作展示了如何使用现代机器学习技术来解决林业行业当前面临的问题。更具体地说,重点是比较几种算法在估算木屑残渣干重(以吨为单位)时的预测性能。该数据集包含在 22 个月内从来自智利 Biobío 和 Maule 地区的 17 个不同农场的 220 辆卡车获得的样本。卡车到达后,收集样品并干燥以计算干吨由每辆卡车携带,这被设置为因变量。使用最先进算法的开源软件实现,可以确定,对于我们的数据,即使非参数模型梯度提升机 (GBM) 和神经网络 (NNET) 优于线性回归 ( LM) 模型,它们在统计上并不优于 LASSO 回归 (GLMNET),这是 LM 模型的改进版本。此外,据观察,季节性会影响卡车在一年中可以运送到发电厂的绿吨与干吨的比率。最后,连续变量绿吨、海拔、东(经纬度)也有助于解释因变量。

更新日期:2021-03-14
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