Exploring the Potential of Machine Learning for Modeling Growth Dynamics in an Uneven-Aged Forest at the Level of Diameter Classes: A Comparative Analysis of Two Modeling Approaches

Growth models of uneven-aged forests on the diameter class level can support silvicultural decision making. Machine learning brings added value to the modeling of dynamics at the stand or individual tree level based on data from permanent plots. The objective of this study is to explore the potential of machine learning for modeling growth dynamics in uneven-aged forests at the diameter class level based on inventory data from practice. Two main modeling approaches are conducted and compared: (i) fine-tuned linear models differentiated per diameter class, (ii) an artificial neural network (multilayer perceptron) trained on all diameter classes. The models are trained on the inventory data of the Canton of Neuchatel (Switzerland), which are area-wide data without individual tree-level growth monitoring. Both approaches produce convincing results for predicting future diameter distributions. The linear models perform better at the individual diameter class level with test R-2 typically between 50% and 70% for predicting increments in the numbers of stems at the diameter class level. From a methodological perspective, the multilayer perceptron implementation is much simpler than the fine-tuning of linear models. The linear models developed in this study achieve sufficient performance for practical decision support.

Automated summary

This study explores the potential of machine learning for modeling growth dynamics in uneven-aged forests at the diameter class level based on inventory data. The results show that machine learning approaches can successfully predict future diameter distributions, and linear models perform better at the individual diameter class level.