Models for dominating forest cover type prediction

The question of the most suitable forest tree species for defined area and landscape has been investigated in the paper. A set of classifiers is constructed in order to build relations between type of soil and other features of forest area and preferable species of trees. The decision tree classifiers, ensemble methods implementing bagging and boosting over such trees are used. The machine learning methods are implemented to obtain the best suited tree species to cover given forest area. This classification task is one of very important problems of forest regeneration process. Efforts of ecologists can have better results if there are expert systems allowing to understand the best forest cover type for areas of forest fires or deforestation that takes place because of human factor. Results and conclusions of this paper can be used in processing of other forest recover tasks. The same methods can be implemented in order to get the preferable tree species for different areas if there's enough data to solve these tasks with machine learning technique.