Stochastic stem bucking using mixture density neural networks

TL;DR

This paper introduces a stochastic bucking method using mixture density neural networks to improve decision-making in forest harvesting by predicting stem profiles with multiple possible outcomes, leading to better log selection.

Contribution

It develops a stochastic bucking algorithm based on LSTM neural networks that generate multiple stem profile predictions, enhancing bucking decisions over traditional models.

Findings

Stochastic LSTM models outperform deterministic and polynomial models in bucking decisions.

Conditioning predictions on multiple measurements improves accuracy.

The method is validated on four coniferous species in eastern Canada.

Abstract

Poor bucking decisions made by forest harvesters can have a negative effect on the products that are generated from the logs. Making the right bucking decisions is not an easy task because harvesters must rely on predictions of the stem profile for the part of the stems that is not yet measured. The goal of this project is to improve the bucking decisions made by forest harvesters with a stochastic bucking method. We developed a Long Short-Term Memory (LSTM) neural network that predicted the parameters of a Gaussian distribution conditioned on the known part of the stem, enabling the creation of multiple samples of stem profile predictions for the unknown part of the stem. The bucking decisions could then be optimized using a novel stochastic bucking algorithm which used all the stem profiles generated to choose the logs to generate from the stem. The stochastic bucking algorithm was compared to two benchmark models: A polynomial model that could not condition its predictions on more than one diameter measurement, and a deterministic LSTM neural network. All models were evaluated on stem profiles of four coniferous species prevalent in eastern Canada. In general, the best bucking decisions were taken by the stochastic LSTM models, demonstrating the usefulness of the method. The second-best results were mostly obtained by the deterministic LSTM model and the worst results by the polynomial model, corroborating the usefulness of conditioning the stem curve predictions on multiple measurements.