Issue |
E3S Web Conf.
Volume 583, 2024
Innovative Technologies for Environmental Science and Energetics (ITESE-2024)
|
|
---|---|---|
Article Number | 02018 | |
Number of page(s) | 7 | |
Section | Pollution and Waste, Weather and Climate | |
DOI | https://doi.org/10.1051/e3sconf/202458302018 | |
Published online | 25 October 2024 |
Predicting tree survival in agroforestry systems using machine learning classification algorithms
1 Reshetnev Siberian State University of Science and Technology, 660037, Krasnoyarsk, Russia.
2 Bauman Moscow State Technical University, Artificial Intelligence Technology Scientific and Education Center, 105005 Moscow, Russia
3 Krasnoyarsk State Agrarian University 660049, Krasnoyarsk, Russia
* Corresponding author: rhfdwjdr1@gmail.com
This article discusses the application of machine learning algorithms to predict the survival of trees in agroforestry systems. Forests play a key role in maintaining ecological balance and biodiversity, but their survival is subject to many threats, including climate change, anthropogenic impacts, diseases and pests. The study used a dataset containing data on various factors affecting the survival of trees, such as the content of phenols, the presence of arbuscular mycorrhizal fungi (AMF), lignin and non- structural carbohydrates (NSC). The classification model was built using the C4.5 decision tree algorithm, which demonstrated high accuracy (86.02%) in predicting the survival of trees. Correlation analysis revealed that phenols and AMF are the most significant factors determining the survival of trees. These results highlight the importance of biochemical and symbiotic factors for tree health. The article also discusses the importance of various factors and suggests directions for future research aimed at improving the management of forest ecosystems in agroforestry systems. The use of machine learning methods allows not only to improve the accuracy of forecasting, but also to develop more effective strategies for the conservation and sustainable management of forests.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.