A Three-Step Neural Network Artificial Intelligence Modeling Approach for Time, Productivity and Costs Prediction
Author(s)
Proto, Andrea Rosario
Zimbalatti, Giuseppe
Macrì, Giorgio
Date Issued
2020
Type
article
Volume
41
Issue
1
Start Page
35
End Page
47
Abstract
The improvement of harvesting methodologies plays an important role in the optimization of
wood production in a context of sustainable forest management. Different harvesting methods
can be applied according to forest site-specific condition and the appropriate mechanization
level depends on a number of factors. Therefore, efficiency and functionality of wood harvesting
operations depend on several factors. The aim of this study is to analyze how the different
harvesting processes affect operational costs and labor productivity in typical small-scale
Italian harvesting companies. A multiple linear regression model (MLR) and artificial neural
network (ANN) have been carried out to predict gross time, productivity and costs estimation
in a series of qualitative and quantitative variables. The results have created a correct statistical
model able to accurately estimate the technical parameters (work time and productivity)
and economic parameters (costs per unit of product and per hectare) useful to the forestry
entrepreneur to predict the results of the work in advance, considering only the values detectable
of some characteristic elements of the worksite.
Subjects