# Optimized Weighted Ensemble Approach for Enhancing Gold Mineralization Prediction  
[PERSON]  
1 State Key Laboratory Breeding Base for Mining Disaster Prevention and Control, Shandong University of Science and Technology, Qingdao 266590, China; [EMAIL_ADDRESS] (M.M.Z. ); [EMAIL_ADDRESS] (J.Z. ); fengfan@[EMAIL_ADDRESS] (F.F. ); [EMAIL_ADDRESS] (L.Q. ); [EMAIL_ADDRESS] (L.J.) 2 College of Energy and Mining Engineering, Shandong University of Science and Technology, Qingdao 266590, China  
3 Mining and Petroleum Engineering Department, Faculty of Engineering, Al-Azhar University, Cairo 11884, Egypt  
4 Faculty of Computers and Artificial Intelligence, Beni-Suef University, Beni-Suef 62521, Egypt; [EMAIL_ADDRESS]  
5 Correspondence: [EMAIL_ADDRESS]; Tel. : +86-1373095196  
Traditional geostatistical methods, such as ordinary and indicator kriging, are commonly used in grade estimation. These techniques are based on spatial interpolation of data and the assumption that the spatial pattern of grade distribution is stationary. While these methods have been used successfully for a long time, they have some significant drawbacks. For example, they require that the data meet certain assumptions, they do not use all of the available data, and they cannot find complex, nonlinear correlations [3]. The difficulties and limitations of conventional geostatistical methods for estimating ore grade have prompted researchers to investigate alternative approaches. In recent years, various computational learning approaches have been developed that can forecast ore grades more correctly without relying on underlying assumptions. These techniques have proved successful in the mining industry [4]. Machine learning (ML) models are a category of computational algorithms with significant potential for use in ore grade estimation in the mining industry. ML models learn from data and then use that knowledge to predict previously unknown data. They can be more adaptable than conventional statistical models when they include and combine various variables, such as geological, geophysical, and geochemical data [5]. This improves mining ore grade estimate. Nevertheless, one of the limitations of ML models is that they need to be fed an adequate amount of data to extract useful features. In addition, machine learning models may be computationally costly, and training and optimizing them might require a lot of computing resources. Machine learning (ML) is a promising approach for spatial estimates in various domains by a large number of research. The most often used machine learning models include artificial neural networks, support vector machines, k-nearest neighbors, cubist, Gaussian process regression, and random forests [6; 7; 8; 9; 10; 11; 12; 13; 14; 15]. These models have been utilized in the process of spatial estimate in a variety of different domains, including mineral prediction. However, typical ML approaches may have performance difficulties when working with non-linear, inconsistent, or higher-dimensional data. Researchers have developed hybrid and ensemble models as a solution to these problems. These models combine traditional ML methods with additional soft computing approaches or use bagging and boosting methodology. The benefits of many soft computing approaches may be combined into a single method through the process of hybridization, which can result in increased productivity. These hybrid approaches have shown to be particularly effective when dealing with huge, multi-dimensional, and non-stationary datasets [16; 17; 18; 19; 20; 21; 22; 23; 24]. The need for a more reliable, consistent, and objective method of estimating gold content in exploration projects has led to the development of a hybrid system based on feature optimization and prediction. Both the feature optimization technique and the prediction strategy employ a hybrid model that integrates different ML models to achieve improved accuracy. The feature optimization approach is responsible for selecting the most pertinent characteristics for prediction.