دورية أكاديمية
أعرض في EDS

Optimized Weighted Ensemble Approach for Enhancing Gold Mineralization Prediction

التفاصيل البيبلوغرافية
العنوان: Optimized Weighted Ensemble Approach for Enhancing Gold Mineralization Prediction
المؤلفون: M. M. Zaki, Shaojie Chen, Jicheng Zhang, Fan Feng, Liu Qi, Mohamed A. Mahdy, Linlin Jin
المصدر: Applied Sciences, Vol 13, Iss 13, p 7622 (2023)
بيانات النشر: MDPI AG, 2023.
سنة النشر: 2023
المجموعة: LCC:Technology
LCC:Engineering (General). Civil engineering (General)
LCC:Biology (General)
LCC:Physics
LCC:Chemistry
مصطلحات موضوعية: machine learning, kriging, MPA optimizer, log normalization, hybrid algorithm, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999
الوصف: The economic value of a mineral resource is highly dependent on the accuracy of grade estimations. Accurate predictions of mineral grades can help businesses decide whether to invest in a mining project and optimize mining operations to maximize the resource. Conventional methods of predicting gold resources are both costly and time-consuming. However, advances in machine learning and processing power are making it possible for mineral estimation to become more efficient and effective. This work introduces a novel approach for predicting the distribution of mineral grades within a deposit. The approach integrates machine learning and optimization techniques. Specifically, the authors propose an approach that integrates the random forest (RF) and k-nearest neighbor (kNN) algorithms with the marine predators optimization algorithm (MPA). The RFKNN_MPA approach uses log normalization to reduce the impact of extreme values and improve the accuracy of the machine learning models. Data segmentation and the MPA algorithm are used to create statistically equivalent subsets of the dataset for use in training and testing. Drill hole locations and rock types are used to create each model. The suggested technique’s performance indices are superior to the others, with a higher R-squared coefficient of 59.7%, a higher R-value of 77%, and lower MSE and RMSE values of 0.17 and 0.44, respectively. The RFKNN_MPA algorithm outperforms geostatistical and conventional machine-learning techniques for estimating mineral orebody grades. The introduced approach offers a novel solution to a problem with practical applications in the mining sector.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2076-3417
Relation: https://www.mdpi.com/2076-3417/13/13/7622; https://doaj.org/toc/2076-3417
DOI: 10.3390/app13137622
URL الوصول: https://doaj.org/article/33b5cd14b9d94735b3ee4a5d87736907
رقم الأكسشن: edsdoj.33b5cd14b9d94735b3ee4a5d87736907
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:20763417
DOI:10.3390/app13137622