Abstract
This paper explores the potential of machine learning in predicting Basal Area Increment (BAI) for the species Abies spectabilis, a commonly used metric for measuring tree growth. Machine learning algorithms are used to analyze environmental factors, biotic responses, growth, and their interactions to obtain accurate predictions of BAI under different climatic scenarios. The study aims to identify vulnerable tree at risk of dieback due to changes in climate or other environmental factors. The methodology includes data acquisition, preprocessing, feature selection, model development, and evaluation. The results reported in the study show M5’ performs better in predicting BAI than other machine learning models. The study’s findings can help in forest management and conservation decisions, such as selective harvesting, reforestation, carbon sequestration, and biodiversity conservation.
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Acknowledgements
The authors would like to thank the Spanish Ministry of Science and Innovation and the Junta de Andalucía for their support within the projects PID2020-117954RB-C21 and TED2021-131311B-C22, PY20-00870, UPO-138516, respectively. The authors would also like to thank the Fundación Tatiana Pérez de Guzmán el Buenofor the support offered through the Beca Predoctoral en Medioambiente de 2018.
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Casas-Gómez, P., Martínez-Álvarez, F., Troncoso, A., Linares, J.C. (2023). Machine Learning Approaches for Predicting Tree Growth Trends Based on Basal Area Increment. In: García Bringas, P., et al. 18th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2023). SOCO 2023. Lecture Notes in Networks and Systems, vol 749. Springer, Cham. https://doi.org/10.1007/978-3-031-42529-5_22
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DOI: https://doi.org/10.1007/978-3-031-42529-5_22
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