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Modeling the productivity of mechanized CTL harvesting with statistical machine learning methods

Liski, Eero and Jounela, Pekka and Korpunen, Heikki and Sosa, Amanda and Lindroos, Ola and Jylhä, Paula (2020). Modeling the productivity of mechanized CTL harvesting with statistical machine learning methods. International Journal of Forest Engineering. 31 , 253-262
[Research article]

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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' onboard 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% R-2, whereas OLS and the SVM machine reached R-2-values of 89.3% and 87% R-2, 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.

Authors/Creators:Liski, Eero and Jounela, Pekka and Korpunen, Heikki and Sosa, Amanda and Lindroos, Ola and Jylhä, Paula
Title:Modeling the productivity of mechanized CTL harvesting with statistical machine learning methods
Series Name/Journal:International Journal of Forest Engineering
Year of publishing :2020
Volume:31
Page range:253-262
Number of Pages:10
Publisher:TAYLOR & FRANCIS INC
ISSN:1494-2119
Language:English
Publication Type:Research article
Article category:Scientific peer reviewed
Version:Published version
Copyright:Creative Commons: Attribution-Noncommercial-No Derivative Works 4.0
Full Text Status:Public
Subjects:(A) Swedish standard research categories 2011 > 4 Agricultural Sciences > 401 Agricultural, Forestry and Fisheries > Forest Science
Keywords:Productivity, cut-to-length, harvester, machine learning, gradient boosted machine, support vector machine, regression model
URN:NBN:urn:nbn:se:slu:epsilon-p-109526
Permanent URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-p-109526
Additional ID:
Type of IDID
DOI10.1080/14942119.2020.1820750
Web of Science (WoS)000595041800011
ID Code:21264
Faculty:S - Faculty of Forest Sciences
Department:(S) > Department of Forest Biomaterials and Technology
Deposited By: SLUpub Connector
Deposited On:18 Jan 2021 13:43
Metadata Last Modified:18 Jan 2021 13:51

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