# Optimized Weighted Ensemble Approach for Enhancing Gold Mineralization Prediction
## 4 Conclusions  
The proposed hybrid model is an innovative approach for predicting mineral grade distribution that can capture nonlinearity and spatial heterogeneity. It has produced superior estimates compared to standalone machine learning or kriging models. The comparative analysis with various machine learning models and geostatistical methods, such as OK and IK, highlights the hybrid model's advantages in reducing errors resulting from incomplete or inconsistent drill hole assay data. The study focuses on the hybrid model's ability to enhance accuracy.  
MPA optimization algorithms play a crucial role in the proposed hybrid model, reducing computational time and enhancing model efficiency and scalability. The MPA optimizer can improve the hybrid method's predictions by adjusting the integration weights, which is particularly beneficial when data are limited or uncertain.  
Log normalization can improve the overall performance of ML models by reducing the impact of extreme values and reducing data skewness. It stabilizes data variance, which is beneficial for models that require a constant variance, and simplifies model interpretation with more intuitive converted values.  
Incorporating factors such as rock types into the model, in addition to the coordinates, can enhance its accuracy by providing more information about the mineral deposit's properties and accounting for spatial variations that may impact the distribution of mineral grades. This is particularly beneficial for complex or heterogeneous mineral deposits.  
The RFKNN-MPA algorithm is a robust and accessible method for estimating mineral orebody grades. It exhibits higher R-squared coefficients and lower RMSE and R values, resulting in superior estimations. These statistics indicate that it can lead to more confident decision making in mining operations. The proposed method preserves patterns within the data by considering geological characteristics (rock type) and chemical composition (gold grade).  
Overall, the suggested hybrid model is a promising approach for predicting the distribution of mineral grades. This method can potentially improve decision making in mining operations and has broader implications in various industries. However, it is essential to continue evaluating and improving the method to ensure its accuracy and usefulness in various settings.  
Conceptualization, M.M.Z. and J.Z.; methodology, M.M.Z.; software, M.M.Z. and M.A.M.; validation, M.M.Z. and S.C.; analysis, M.M.Z. and S.C.; data curation, M.M.Z., F.F., and L.Q.; writing--original draft preparation, M.M.Z.; writing--review and editing, M.M.Z., S.C., J.Z., F.F., M.A.M., L.Q. and L.J.; visualization, M.M.Z.; supervision S.C.; funding acquisition S.C. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the National Natural Science Foundation of China (No. 52074169, 52004143, and 52174159).  
Not applicable.  
This data is confidential and owned by the company, and we do not have the right to publish it.  
The authors wish to extend their appreciation to Centamin PLC for supplying the case study dataset that was instrumental in the success of this research project. Additionally, the authors are thankful to [PERSON] and [PERSON] for their enthusiastic support and insightful feedback during the manuscript review process.  
The authors declare no conflict of interest.