Predicting Moisture Content on Wood Using Machine Learning Classification Methods

Vítor Magalhães, Giancarlo Lucca, Alessandro Bicho, Eduardo Nunes Borges

2023

Abstract

The growing demand for wood in several industry segments and for its economical value increased illegal deforestation in several countries. As a direct consequence, climate changes across the planet have been aggravated, which further increases the prominence and concern about the issue of deforestation. So that these potentially catastrophic effects can be mitigated, it is necessary to better use wood in production processes. In this sense, a key point is the variation of the moisture content of the wood as a function of storage time, since, as the wood logs are stored outdoors, they gradually begin to lose water. Dry wood usually cracks, which makes most of its use unfeasible – depending on the purpose – which can even lead to the disposal of the log. Considering that there is a direct relationship between moisture content and wood weight, this work aims to develop different possible solutions for this problem using explainable machine learning methods, contributing to the effectiveness in controlling the variation in moisture content and, consequently, to a better use in the production processes in which wood is used as a raw material.

Paper Citation

in Harvard Style

Magalhães V., Lucca G., Bicho A. and Nunes Borges E. (2023). Predicting Moisture Content on Wood Using Machine Learning Classification Methods. In Proceedings of the 25th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-648-4, SciTePress, pages 607-614. DOI: 10.5220/0011988600003467

in Bibtex Style

@conference{iceis23,
author={Vítor Magalhães and Giancarlo Lucca and Alessandro Bicho and Eduardo Nunes Borges},
title={Predicting Moisture Content on Wood Using Machine Learning Classification Methods},
booktitle={Proceedings of the 25th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2023},
pages={607-614},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011988600003467},
isbn={978-989-758-648-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 25th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - Predicting Moisture Content on Wood Using Machine Learning Classification Methods
SN - 978-989-758-648-4
AU - Magalhães V.
AU - Lucca G.
AU - Bicho A.
AU - Nunes Borges E.
PY - 2023
SP - 607
EP - 614
DO - 10.5220/0011988600003467
PB - SciTePress