Optimizing Mean Fragment Size Prediction in Rock Blasting: A Synergistic Approach Combining Clustering, Hyperparameter Tuning, and Data Augmentation

Accurate estimation of the mean fragment size is crucial for optimizing open-pit mining operations. This study presents an approach that combines clustering, hyperparameter optimization, and data augmentation to enhance prediction accuracy using the Xtreme Gradient Boosting (XGBoost) regression mode...

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Autors principals:	Ian Krop, Takashi Sasaoka, Hideki Shimada, Akihiro Hamanaka
Format:	Artigo
Idioma:	Inglês
Publicat:	MDPI AG 2024-08-01
Col·lecció:	Eng
Matèries:	machine learning regression clustering data augmentation XGBoost hyperparameter tuning
Accés en línia:	https://www.mdpi.com/2673-4117/5/3/102
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