## 5 Discussion  
### Discussion and Interpretation of Results  
Based on the results, we can draw the following conclusions:  
* The ANN model achieved the highest accuracy on the test data (R\({}^{2}\) = 0.996), but in practice its use may be limited by the need for more training data and lower interpretability of the results. Similar findings are present on the high accuracy of ANN; however, limitations in the area of interpretability and the need for large amounts of training data are reported by [48; 68]. In conditions with less data or significantly different operating conditions, it may be more appropriate to use a more robust random forest model that is less prone to overlearning and better manages input variability. For higher robustness and the ability to handle input variability, it is also appropriate to consider the use of random forest models in industrial practice, as documented by articles [34; 69]. * The regression model and the regression tree model provide simpler and more transparent relationships between inputs and output, which is advantageous when there is a need for quick diagnostics or explanation of decisions in industrial practice. On the other hand, these models may have a limited ability to capture complex nonlinear relationships. The contribution and suitability of regression and decision trees for quick diagnostics and explainability are confirmed by [24; 70]. * When deployed in industry, it is also necessary to take into account the robustness of the models against noise, the ability to adapt to new types of damage or changes in operating conditions. The ANN model is sensitive to the quality of the input data and may require more frequent recalibration. The random forest model is more computationally intensive, which can be limiting when the fast processing of large volumes of data in real time is required. These aspects are pointed out by, for example, [31; 34; 69]. In terms of the importance of input variables in determining the conveyor belt impact resistance model, there are significant differences between the models. It is important to realize that each model determines the importance or significance of the variables to predict the output variable in a different way, and therefore we will not consider absolute values but the order of importance. In all the models, the impact height of the hammer has the most significant influence, and in all models, the negligible influence of the impactor of the hammer on the prediction is shown. A summary of the importance of the input variables in the creation of the model and in the prediction of the impact force is in Table 12.  
The impact height is the most significant input in all models. This parameter most influences the resulting impact force and its leading position corresponds to basic physical expectations. The dominant influence of the impact height on belt impact resistance is in agreement with the results [57; 68]. Weight of the hammer is the second most significant \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Importance of Variables–Order**} \\ \hline
**Model** & **Regression Model** & **Decision Tree Regression Model** & **Random Forest Model** & **ANN Model** \\ \hline Impact height & 1. & 1. & 1. & 1. \\ Weight of the hammer & 2. & 2. & 2. & 3. \\ Impactor of the hammer & 4. & \multicolumn{1}{c}{Insignificant} & \multicolumn{1}{c}{Insignificant} & \multicolumn{1}{c}{Insignificant} \\ CB Strength & 3. & \multicolumn{1}{c}{Insignificant} & 3. & 2. \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Importance of input variables. parameter in most models, but in ANN models it is only in third place. The impactor type is insignificant in all models, which means that its variability does not bring a fundamental change in prediction. This result is consistent with research findings [53; 54] and also has practical significance--it allows for simplifying experiments as well as belt design in terms of robustness against the variability of falling material.