## 4 Results
### SHAP Analysis Results  
Prior analysis has demonstrated that the MPA-XGBoost model successfully establishes a comprehensive regression analysis framework by effectively identifying the relationships between various mining influence factors and PC. The model provides an accurate depiction of how different factors impact PC, highlighting the necessity for a deeper exploration of each factor's contribution to PC. To achieve this, the SHAP method was employed to elucidate the importance and contribution of each feature to the PC outcomes, as illustrated in Figure 8.  
To further and more comprehensively elucidate the contribution of each factor in the dataset to the research outcomes, we adopted a systematic analytical approach and conducted an in-depth examination of the overall impact and specific contributions of input factors on PC variations (for details, see Figure 8a,b). In these figures, the color coding serves as a clear indicator: colors closer to blue signify a stronger negative effect on PC, suggesting potential inhibition of or reduction in PC-related metrics; conversely, colors closer to red indicate a more pronounced positive influence on PC, likely enhancing or promoting its metrics. The parameters are arranged from top to bottom according to their overall influence in descending order, providing an intuitive visual comparison. Upon close observation and analysis, it becomes evident that among all the factors, DE exhibits the most significant overall influence. Specifically, DE contributes 35.6% to the total effect, underscoring its critical role and importance within the entire research framework.  
When DE increases, the contribution points turn red, indicating a positive effect on PC; conversely, when DE decreases, the contribution points turn blue, indicating a negative effect. This suggests that DE has a significant positive influence on PC. VT, EMT, DOMQ, and ORE have similar contributions to PC, accounting for 14.2%, 13.9%, 13.7%, and 13.1% of the total impact, respectively. VT and EMT exhibit negative impacts on PC, while DOMQ and ORE show positive impacts. Other factors, such as ST and CBE, have relatively minor contributions to PC, accounting for 3.9% and 5.5%, respectively.  
Figure 8: SHAP analysis (**(a)** The SHAP value ranking summary chart illustrates the descending order of influence exerted by different indicator factors on the outcome. **(b)** The SHAP value ring chart illustrates the extent to which various indicator factors affect the outcome, offering a clear visualization of their relative importance. **(c)** The correlation and interaction between the variations in DE values and SHAP values. **(d)** The correlation and interaction between the variations in VT values and SHAP values. **(e)** The correlation and interaction between the variations in EMT values and SHAP values.).  
To further explore the specific influence patterns of different factors on PC, we selected the three most influential factors and constructed SHAP dependency plots (Figure 8c-e). These plots reveal that DE has a consistently positive impact on PC. Specifically, when DE exceeds 225 (the tipping point), PC shows a significant increase. VT and EMT both have negative impacts on PC. When VT and EMT are below the tipping point, PC decreases gradually; however, when they exceed the tipping point, PC drops sharply. The results suggest that enhancing the PC of the mining area can be achieved by increasing DE and reducing VT and EMT, leading to substantial improvements.