Deep Learning Estimation of the Industrial Wood Production Level with Respect to the Natural Harm Factors

Abstract

Abstract Protection and sustainability of forest assets are possible with planned production of the forest products. Among these products, industrial wood is the most important raw material for various sectors. Thus, the estimation of the industrial wood production level is important to use this product optimally, without disturbing the balance of forest assets. This article aims to estimate the industrial wood production level based on four natural harm factors, which are forest fires, outbreaks of insects, outbreaks of diseases, and severe weather events. The estimation model was built by using a deep learning method, which is an artificial neural network model based on the multilayer perceptron architecture. The study shows that the most harmful factor decreasing the industrial wood production level is outbreaks of diseases. The second effective factor, however, appears to be severe weather events. The third and the fourth factors were determined to be outbreaks of insects and burned forest areas, respectively.