APPLICATION OF MACHINE LEARNING METHODS FOR THE SELECTION OF FOREST TRACKED VEHICLES

Tracked tractors used in logging and forestry are characterized by great variability in design parameters and operational characteristics. The purpose of this study is to substantiate the classification of tracked tractors, for their effective selection for specific natural production conditions of logging and forestry operations, based on the results of data analysis obtained using machine learning algorithms. The classification is proposed based on the markup of objects (tractor models) obtained using the k-means data clustering method. The work was carried out within the framework of the scientific school "Innovative developments in the field of logging industry and forestry".