A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Predictability of boreal forest soil bearing capacity by machine learning
Tekijät: J. Pohjankukka, H. Riihimäki, P. Nevalainen, T. Pahikkala, J. Ala-Ilomäki, E. Hyvönen, J. Varjo, J. Heikkonen
Kustantaja: PERGAMON-ELSEVIER SCIENCE LTD
Julkaisuvuosi: 2016
Journal: Journal of Terramechanics
Tietokannassa oleva lehden nimi: JOURNAL OF TERRAMECHANICS
Lehden akronyymi: J TERRAMECHANICS
Vuosikerta: 68
Aloitussivu: 1
Lopetussivu: 8
Sivujen määrä: 8
ISSN: 0022-4898
DOI: https://doi.org/10.1016/j.jterra.2016.09.001
Tiivistelmä
In forest harvesting, terrain trafficability is the key parameter needed for route planning. Advance knowledge of the soil bearing capacity is crucial for heavy machinery operations. Especially peatland areas can cause severe problems for harvesting operations and can result in increased costs. In addition to avoiding potential damage to the soil, route planning must also take into consideration the root damage to the remaining trees. In this paper we study the predictability of boreal soil load bearing capacity by using remote sensing data and field measurement data. We conduct our research by using both linear and nonlinear methods of machine learning. With the best prediction method, ridge regression, the results are promising with a C-index value higher than 0.68 up to 200 m prediction range from the closest point with known bearing capacity, the baseline value being 0.5. The load bearing classification of the soil resulted in 76% accuracy up to 60 m by using a multilayer perceptron method. The results indicate that there is a potential for production applications and that there is a great need for automatic real-time sensoring in order to produce applicable predictions. (C) 2016 ISTVS. Published by Elsevier Ltd. All rights reserved.
In forest harvesting, terrain trafficability is the key parameter needed for route planning. Advance knowledge of the soil bearing capacity is crucial for heavy machinery operations. Especially peatland areas can cause severe problems for harvesting operations and can result in increased costs. In addition to avoiding potential damage to the soil, route planning must also take into consideration the root damage to the remaining trees. In this paper we study the predictability of boreal soil load bearing capacity by using remote sensing data and field measurement data. We conduct our research by using both linear and nonlinear methods of machine learning. With the best prediction method, ridge regression, the results are promising with a C-index value higher than 0.68 up to 200 m prediction range from the closest point with known bearing capacity, the baseline value being 0.5. The load bearing classification of the soil resulted in 76% accuracy up to 60 m by using a multilayer perceptron method. The results indicate that there is a potential for production applications and that there is a great need for automatic real-time sensoring in order to produce applicable predictions. (C) 2016 ISTVS. Published by Elsevier Ltd. All rights reserved.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
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