Bulletin of Forestry Science / Volume 8 / Issue 1 / Pages 93-103
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Machine learning approximation of Járó-table (table of applicable targeted forest stands and their growth for each forest site)

Kornél Czimber, Csaba Mátyás, András Bidló & Borbála Gálos


Correspondence: Czimber Kornél

Postal address: H-9400 Sopron, Bajcsy-Zsilinszky u. 4.

e-mail: czimber.kornel[at]uni-sopron.hu


In this article, we would like to present a machine learning algorithm that processes the data of Járó’s target stands and their growth for each forest site variation. The method is able to propose stand types and growths on the basis of existing data for new variations due to climate change and for a newly entering forest climate zone. The essence of this process is to place the entries of the Járó’s table in a five-dimensional space, and use distance kernels to select the closest target stand types and weight their growth rate. It defines for a specific forest site, which target stands are likely to be in the area and what kind of growth can be characterized. The results will be incorporated into the decision support system of the Agrárklima project after proper validation.

Keywords: machine learning, forest site, targeted stand, growth

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    Cite this article as:

    Czimber, K., Mátyás, Cs., Bidló, A. & Gálos, B. (2018): Machine learning approximation of Járó-table (table of applicable targeted forest stands and their growth for each forest site). Bulletin of Forestry Science, 8(1): 93-103. (in Hungarian) DOI: 10.17164/EK.2018.006

    Volume 8, Issue 1
    Pages: 93-103

    DOI: 10.17164/EK.2018.006

    First published:
    29 May 2018

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