Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Prediction of Windthrow Damage in Mixed Conifer-Broadleaf Stands via a Machine Learning Model

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. Preprints 2022, 2022020201 (doi: 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 (doi: 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.

Keywords

wind damage; wind disturbance; Pinus sylvestris; Picea abies; machine learning; random forest

Subject

EARTH SCIENCES, Environmental Sciences

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