20.500.12556/RUL-164387 Regresijski model za napovedovanje količine predelanega lesa magistrsko delo Regression model for forecasting quantities of processed wood V magistrski nalogi je razvit regresijski model strojnega učenja za napovedovanje količine predelanega lesa z določenih gozdnih območij. Predelan les se razvršča v dve kategoriji: les za hlode in les za druge izdelke, med drugim drva in celuloza. Ta razdelitev temelji na znatni razliki v vrednosti lesa, saj ima les za hlode bistveno večjo vrednost kot ostali les. Model je prilagojen realnemu procesu, v katerem je najprej izveden natančen popis dreves, ki so namenjena za posek, pri čemer so drevesa razvrščena v kubaturne (debelinske) razrede glede na premer debla. Po poseku je določena količina lesa razdeljena v različne kategorije, kot so hlodi, drva in les za celulozo. Bruto količina, izmerjena pred posekom, in neto količina, izmerjena po poseku, predstavljata osrednji spremenljivki raziskave. Analizi so dodane tudi dodatne značilnosti, kot so vrsta sečnje in tarifni razred, za povečanje natančnosti napovedi. Glavni cilj naloge je razvoj natančnega napovednega modela, ki temelji na zbranih podatkih o poseku in omogoča napovedovanje neto količine posekanega lesa, pripravljenega za transport. Uporabljeni so napredni regresijski modeli, kot so regresor z večplastnim perceptronom, linearna regresija, naključni gozdovi in regresor z gradientno krepitvijo. Metodologija za shranjevanje in nadaljnjo analizo napovedi in rezultatov s pomočjo orodja Power BI prispeva k globljemu razumevanju in analizi dinamike lesnopredelovalne industrije. In the master's thesis, a machine learning regression model is developed to predict the quantity of processed wood from specific forest areas. The processed wood is classified into two categories: timber logs and other products such as firewood or pulpwood. This classification is based on the significant difference in the value of the wood, as timber logs have a much higher value compared to other types of wood. The model is tailored to a real-world process, where a precise inventory of trees designated for felling is first conducted, and the trees are categorized into volumetric (diameter) classes based on the diameter of the trunk. After felling, the quantity of wood is allocated into different categories, such as logs, firewood, and pulpwood. The gross volume measured before felling and the net volume measured after felling represent the central variables of the research. Additional features, such as the type of logging and the tariff class, are also included in the analysis to improve the accuracy of the predictions. The primary goal of the thesis is the development of an accurate predictive model based on the collected harvesting data, enabling the prediction of the net volume of harvested wood ready for transport. Advanced regression models such as the multilayer perceptron regressor, linear regression, random forests, and gradient boosting regressor are used. The methodology for storing and further analyzing predictions and results using Power BI contributes to a deeper understanding and analysis of the dynamics of the wood processing industry. strojno učenje model regresija linearna regresija naključni gozd gradientna krepitvena metoda nevronska mreža gozd drevo količina kubatura napoved machine learning model regression linear regression random forest gradient boosting method neural network forest tree quantity cubature prediction true false false Slovenski jezik Angleški jezik Magistrsko delo/naloga 2024-10-24 08:15:05 2024-10-24 08:15:12 2024-11-22 14:54:09 0000-00-00 00:00:00 2024 0 0 0000-00-00 NiDoloceno NiDoloceno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 004.8 145352 212612355 15033.pdf 15033.pdf 1 DF9F7FDED7D8432D982B4670677AA018 77137782d90e2907ef54f48ca436795cdddd0026da836f45cc5dbea9d8dbcd35 080fc1dc-91cf-11ef-b232-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=194742 Fakulteta za matematiko in fiziko 0 0 0