# Optimized Weighted Ensemble Approach for Enhancing Gold Mineralization Prediction
The MPA is one of the recent optimization algorithms; it is inspired by the optimal foraging strategy between predator and prey in marine ecosystems to solve the optimization. The foraging strategy enables the MPA to converge to the optimal global solution within a convenient time even in multidimensional search spaces. Accordingly, based on the experimental results illustrated in [25], the MPA showed outstanding performance against the old algorithms GA, PSO, and the well-known GSA, CS, SSA, and CMA-ES; furthermore, the MPA was initially evaluated on twenty-nine test functions, test suite of CEC-BC-2017, three engineering benchmarks, and two real-world engineering design problems. These features have motivated researchers to employ the MPA in many various applications, i.e., in [26], the MPA was used to optimize the control parameters of four machine learning (ML) models for predicting the compressive strength (CS) of concrete. In another study [27], authors employed the MPA algorithm to perform a feature selection process; they developed a robust human action recognition framework by integrating deep learning and swarm intelligence. In [28], the MPA was used to optimize three ML models for predicting and analyzing parks and attached green spaces based on a dataset of urban green space. These points motivated us to use the MPA algorithm in the proposed methodology. The primary purpose of this piece of study is to present an innovative method for estimating the mineral resources (prediction of gold content in the Quartz Ridge area). The suggested approach, which will be referred to as RFKNN-MPA, would increase the accuracy of grade estimations by combining the Random Forest (RF) and k-nearest neighbor (kNN) models with optimized using the Marine Predators Optimization Algorithm (MPA). The method incorporates geological data, such as lithology, and geographical data, such as easting, northing, and elevation, into the model creation process. The model-making process consists of three steps. In the first step, the random forest and kNN models generate initial predictions. In the second step, the MPA method is used to optimize the parameters of both the kNN and the random forest models. In the third step, the predictions generated by the optimized versions of the Random Forest and kNN models are merged to provide a final forecast. Once the model has been trained with enough data, it can learn the relationship between the input factors (rock type and geographic position) and the gold grade, the outcome variable of interest. The RFKNN-MPA technique improves grade forecasts and mineral exploration estimates by integrating different models and refining their parameters using the MPA algorithm. To the best of our knowledge, this study is the first attempt to estimate the grade using the optimized weighted integration between KNN and RF regressions optimized by the MPA; further, the experimental results investigated how well the proposed RFKNN-MPA predicted outcomes compared to the results of other machine learning models and geostatistical approaches. The outcomes demonstrated that the RFKNN-MPA method performed better in accuracy and reliability than the other models.