Abstract
The production data of mineral resources are noisy, nonstationary, and nonlinear. Therefore, some techniques are required to address the problem of nonstationarity and complexity of noises in it. In this paper, two hybrid models (EMD-CEEMDAN-EBT-MM and WA-CEEMDAN-EBT-MM) flourish to improve mineral production prediction. First, we use empirical mode decomposition (EMD) and wavelet analysis (WA) to denoise the data. Second, ensemble empirical mode decomposition (EEMD) and complete ensemble empirical mode decomposition (CEEMDAN) are used for the decomposition of nonstationary data into intrinsic mode function (IMF). Then, empirical Bayesian threshold (EBT) is applied on noise dominant IMFs to consolidate noises, which are further used as input in the data-driven model. Next, other noise-free IMFs are used in the stochastic model as input for the prediction of minerals. At last, the predicted IMFs are ensemble for final prediction. The proposed strategy is exemplified using Pakistan's four major mineral resources. To measure the prediction performance of all the models, three methods, that is, mean relative error, mean square error, and mean absolute percentage error, are used. Our proposed framework WA-CEEMDAN-EBT-MM has shown improvement with minimum mean absolute percentage error value compared to other existing models in prediction accuracy for all four minerals. Therefore, our proposed strategy can predict the noisy and nonstationary time-series data with an efficient mechanism. Hence, it will be helpful to the policymakers for making policies and planning in mineral resource management.
Highlights
Academic Editor: Firdous Khan e production data of mineral resources are noisy, nonstationary, and nonlinear. erefore, some techniques are required to address the problem of nonstationarity and complexity of noises in it
empirical mode decomposition (EMD) and wavelet analysis (WA) are the most commonly used preprocessing algorithms for nonlinear or nonstationary data and provide better results. e algorithms of WA decompose the nonlinear and nonstationary data of mineral resources into multiscale components [17]. ese components are used as inputs at the prediction stage, and these predicted components are ensemble for final prediction. e present paper uses EMD and WA-based thresholds to reduce noises from the mineral production data
Results and Discussion is section presents the results of the proposed EMD/WAEEMD-MM and EMD/WA-CEEMDAN-MM in comparison with other selected models
Summary
Two hybrid models (EMD-CEEMDAN-EBTMM and WA-CEEMDAN-EBT-MM) flourish to improve mineral production prediction. Data on the production of mineral resources are nonlinear, noisy, and nonstationary. E development of data-driven models makes it easy to deal with nonstationary and nonlinear time-series data from the study of Lapedes and Farber [5]. To overcome the drawbacks of a data-driven model, hybrid models are introduced that capture the characteristics of varying times and reduce the noises, which eventually improves the accuracy of prediction from the study of Nourani et al [13]; Pramanik et al [14]; Yaseen et al [15]. Hybrid models are the combination of some preprocessing techniques, that is, wavelet analysis (WA), empirical mode decomposition (EMD), and ensemble empirical mode decomposition (EEMD), with data-driven models. EMD and WA are the most commonly used preprocessing algorithms for nonlinear or nonstationary data and provide better results. e algorithms of WA decompose the nonlinear and nonstationary data of mineral resources into multiscale components [17]. ese components are used as inputs at the prediction stage, and these predicted components are ensemble for final prediction. e present paper uses EMD and WA-based thresholds to reduce noises from the mineral production data
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