Version 1
: Received: 16 February 2022 / Approved: 17 February 2022 / Online: 17 February 2022 (05:06:55 CET)
How to cite:
Dimou, V.; Demertzis, K.; Kantartzis, A. Prediction of Windthrow Damage in Mixed Conifer-Broadleaf Stands via a Machine Learning Model. Preprints2022, 2022020201. https://doi.org/10.20944/preprints202202.0201.v1
Dimou, V.; Demertzis, K.; Kantartzis, A. Prediction of Windthrow Damage in Mixed Conifer-Broadleaf Stands via a Machine Learning Model. Preprints 2022, 2022020201. https://doi.org/10.20944/preprints202202.0201.v1
Dimou, V.; Demertzis, K.; Kantartzis, A. Prediction of Windthrow Damage in Mixed Conifer-Broadleaf Stands via a Machine Learning Model. Preprints2022, 2022020201. https://doi.org/10.20944/preprints202202.0201.v1
APA Style
Dimou, V., Demertzis, K., & Kantartzis, A. (2022). Prediction of Windthrow Damage in Mixed Conifer-Broadleaf Stands via a Machine Learning Model. Preprints. https://doi.org/10.20944/preprints202202.0201.v1
Chicago/Turabian Style
Dimou, V., Konstantinos Demertzis and Apostolos Kantartzis. 2022 "Prediction of Windthrow Damage in Mixed Conifer-Broadleaf Stands via a Machine Learning Model" Preprints. https://doi.org/10.20944/preprints202202.0201.v1
Abstract
Management approaches inspired by the variability of natural disturbances are expected to produce forests in the future that will be significantly more resilient and better adapted to local environmental conditions. Due to climate change, windstorms are becoming increasingly common resulting in the destruction not only of extensive forest areas but, quite often, of small-sized and scattered forest lands that can ultimately become home to insects and disease dissemination sites. In the present study, an attempt is made to identify and record areas in the northeastern forests of Greece covered by mixed stands of conifers and broadleaves that experienced massive windthrow following local storms. Based on tree-level data, local topographic features, forest characteristics and the mechanical properties of green wood, a reliable model, to be used for the prediction of similar disturbances in the future, has been created after a thorough comparative study of the most well-known intelligent machine learning algorithms.
Environmental and Earth Sciences, Environmental Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
