## 4 Result  
### Definition of Variables and Selection of ML Algorithms  
The aim of the research is to quantify the influence of the selected parameters on the impact force that arises when the material hits the conveyor belt. The input variables represent the height of the hammer's impact, the weight of the falling hammer's impact on the conveyor belt, the type of impactor of the hammer and the strength of the conveyor belt. A basic overview of input and output variables is in Table 2.  
The relationships between these variables were investigated using various machine learning methods: multiple regression model, decision tree regression, random forest, and artificial neural network. In all cases, the process of creating the model was performed according to the schema in Figure 6.  
The measured data are divided into two groups: the training group (70% of the data) and the test group (30% of the data). The training group is used to create a model, while the test group is used to evaluate the quality of the model. Each model was created on a training group consisting of 70% of randomly selected experimental measurements (260 measurements). The accuracy and quality of the model was verified on the test group, which consisted of the remaining 30% of the experimental measurements (112 measurements). The performance of individual models was evaluated using standard metrics: MAE, MSE, RMSE and R\({}^{2}\).  
Each of the considered algorithms, which result in 4 different models (regression model, decision tree regression model, random forest model and ANN model), represents a different approach to solving regression problems with different levels of complexity, interpretability, and ability to capture nonlinear relationships:  
* The regression model is the result of a basic method (regression analysis) that enables simple interpretation of the relationships between the input and output variables and is traditionally used in technical applications.
* The decision tree regression model is a simple and interpretable model, often used for rapid data segmentation and basic prediction.
* The random forest model is the result of a method that improves the robustness and accuracy of prediction by combining multiple trees, thereby overcoming the limitations of individual tree models.
* The ANN model has been included as a result of an advanced machine learning algorithm that can efficiently model comprehensive and nonlinear relationships in data and has proven itself in many engineering applications in recent years.  
\begin{table}
\begin{tabular}{c c} \hline
**Variables** & **Description** \\ \hline Dependent Variables & \\ H & Impact height of the hammer (m) \\ W & Weight of the falling hammer (kg) \\ I & Type of impactor of the hammer \\ I & (spherical, pyramidal) \\ S & Conveyor belt strength (N\(\cdot\)mm\({}^{-1}\)) \\ Independent Variables & \\ F & Impact force (kN) \\ \hline \end{tabular}
\end{table}
Table 2: Overview of input and output variables.  
Figure 6: Principle of model creation.  
The choice of these algorithms thereby enables a comprehensive comparison from simpler to more complex algorithms that cover a wide range of approaches used in current research on the impact resistance of conveyor belts. In addition, these models are well supported by the available libraries and enable efficient training and validation on an experimental database.