Development of Hybrid Methods for Prediction of Principal Mineral Resources. (9th August 2021)

• Record Type: Journal Article
• Authors: Qurban, Maria; Zhang, Xiang; Nazir, Hafiza Mamona; Hussain, Ijaz; Faisal, Muhammad; Elashkar, Elsayed Elsherbini; Khader, Jameel Ahmad; Soudagar, Sadaf Shamshoddin; Shoukry, Alaa Mohamd; Al-Deek, Fares Fawzi
• Other Names: Li Yuxing Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2021(2021)
• Publication Date: 2021-08-09
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2021/6362660
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : Accurate estimation of the mining process is vital for the optimal allocation of mineral resources. The development of any country is precisely connected with the management of mineral resources. Therefore, the forecasting of mineral resources contributed much to management, planning, and a maximum allocation of mineral resources. However, it is challenging because of its multiscale variability, nonlinearity, nonstationarity, and high irregularity. In this paper, we proposed two revised hybrid methods to address these issues to predict mineral resources. Our methods are based on denoising, decomposition, prediction, and ensemble principles that are applied to the production of mineral resource time-series data. The performance of the proposed methods is compared with the existing traditional one-stage model (without denoised and decomposition strategies) and two-stage hybrid models (based on denoised strategy), and three-stage hybrid models (with denoised and decomposition strategies). The performance of these methods is evaluated using mean relative error (MRE), mean absolute error (MAE), and mean square error (MSE) as evaluation measures for the production of four principle mineral resources of Pakistan. It is concluded that the proposed framework for the prediction of mineral resources indicated better performance as compared to other existing one-stage, two-stage, and three-stage models. Furthermore, the prediction accuracy of the revised hybrid model is improved by reducing the complexity of the production of mineral resource time-series data.