Localizing the Base Learner weights in Ensemble Methods to Improve the Grade Modeling Accuracy | ||
| Journal of Mining and Environment | ||
| مقاله 17، دوره 17، شماره 1، فروردین و اردیبهشت 2026، صفحه 321-332 اصل مقاله (5.34 M) | ||
| نوع مقاله: Original Research Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jme.2024.15242.2918 | ||
| نویسندگان | ||
| Ahmadreza Erfan؛ Saeed Soltani Mohammad* ؛ Maliheh Abbaszadeh | ||
| Department of Mining Engineering, University of Kashan, Iran | ||
| چکیده | ||
| Machine learning (ML) has significantly transformed multiple disciplines, including mineral resource evaluation in mining engineering, by facilitating more accurate and efficient estimation methods. Ensemble methods, as a fundamental component of modern machine learning, have emerged as powerful tools that robust techniques that integrate multiple predictive models to improve performance beyond that of any individual learner. This study proposes a novel ensemble method for estimating ore grades by localizing the base learner weights in ensemble method. Ordinary kriging, inverse distance weighting, k-nearest neighbors, support vector regression, and artificial neural networks have been used as the base learners of the algorithm. In ML base learners, coordinates (easting, northing and elevation) of samples have been defined as input nodes and grade has been defined as target. The proposed method has been validated for predicting the copper grade (Cu%) in Darehzar porphyry deposit. The performance of proposed method has been by individual base learners and famous ensemble methods. This comparison shows that performance of proposed method is better than other ones. The findings highlight the necessity of adapting ensemble methods to address spatial variability in geological data, thereby establishing a robust framework for ore grade estimation. | ||
| کلیدواژهها | ||
| Ore grade estimation؛ Machine learning algorithms؛ Ensemble model؛ Base learner؛ Local variability | ||
| مراجع | ||
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