Prediction of Forestry Planned End Products Using Dirichlet Regression and Neural Networks

We describe a set of nonparametric and machine learning models to forecast the proportion of planned end products (PEP) that can be extracted from a forest compartment. We determine which forest crop attributes are significant in predicting the product proportions (of sawlog, pallet, stake, and pulp) based on an Irish data set supplied by Coillte, the Irish state forestry company. Dirichlet regression and neural networks are applied to predict the product proportions and evaluated against a multivariate multiple regression benchmark model. Based on predictive performance, the neural network performs slightly better in comparison to Dirichlet regression. However, assessing the model logic and taking account of user interpretation, the Dirichlet regression outperforms the neural network. Both models are also compared to an existing rule-based model used by Coillte. The nonparametric and machine learning techniques provided consistent reliable models to accurately predict the PEP proportions. The two proposed models extend the versatility of nonparametric and machine learning techniques to areas such as forestry.