## 2 Related Work
### Research Gap and Contribution/Novelty of This Study  
Despite the advancements, a significant gap remains in the quantitative, systematic evaluation of the impact of key physical parameters on the impact force and resistance of belts under real (experimental) conditions. Despite advancements in monitoring and predictive maintenance of conveyor belts, a significant gap still exists in the quantitative and systematic evaluation of the impact of key parameters on the impact force and resistance of belts under controlled, yet real experimental conditions. This is an area where comprehensive and precise modeling of all relevant factors still lag. Most works declare the significance of drop height, load weight, or belt strength [24; 52], but the precise determination of their influence within a complex model or experiment is rare. Truly detailed analytical and experimental evaluation is still the subject of newer works [53; 54; 55]. However, significant research results in the field of conveyor belt impact resistance have also been brought by systematically oriented studies [56; 57; 58; 59]. [PERSON] et al. [56] thoroughly analyze methods for evaluating the impact resistance of belts to the dynamic action of concentrated load and provide a detailed experimental description of the influence of individual material parameters on belt lifetime. Further studies [57; 58] focus on quantifying the influence of structural properties and operating conditions on the fatigue strength of joints or the degradation and damage of belts, with emphasis placed on evaluating belt behavior under combined or repeated impact loads. The results of laboratory tests of joints and critical areas of belts [59] thoroughly analyze the resistance of textile belts to damage in harsh conditions of underground mines. These latest analytical and experimental publications have significantly advanced the understanding of the influence of individual parameters on impact resistance; however, the need for quantitative modeling of a wider range of factors--and their integration into complex predictive frameworks--still persists. The study's results significantly refine existing knowledge about the influence of individual input parameters on conveyor belt impact resistance. While many existing (and also our previous) studies focused primarily on the question \"Which input parameter influences conveyor belt impact resistance?\", this study provides an answer to a fundamentally more detailed question: \"How, or to what extent, do individual input parameters influence conveyor belt impact resistance?\"--and that systematically and across multiple machine learning model approaches. In comparison with available scientific works, this study contributes to the development of knowledge in the field of conveyor belt impact resistance prediction in the following ways:  
* Complex comparison of ML approaches: We perform a deep analysis and comparison of four different machine learning approaches (regression analysis, decision tree, random forest, and artificial neural network). This gives us a comprehensive view of their ability to capture different types of relationships--from linear to complex nonlinear and interactive relationships typical for material mechanics and impact dynamics. Systematic testing on identical data increases prediction reliability and allows for the selection of the most robust model for specific applications. * Use of original experimental data: The data used in the study is unique, obtained directly by experiment on a special device from testing real rubber-textile belts. This aspect significantly increases the relevance and credibility of the results. * In-depth analysis of model performance: We compare the performance of algorithms not only based on standard metrics (MSE, RMSE, R2), but also based on a thorough analysis of residuals and monitoring of systematic errors. This detailed approach provides a more comprehensive view of the robustness and reliability of predictive capabilities, which is crucial for practical applications.