Hybrid machine learning approach for accurate prediction of the drilling rock index.

Autor: Shahani NM; School of Mines, China University of Mining and Technology, Xuzhou, 221116, China., Zheng X; School of Mines, China University of Mining and Technology, Xuzhou, 221116, China. cumt_ckzxg@126.com.; The State Key Laboratory for Geo Mechanics and Deep Underground Engineering, China University of Mining & Technology, Xuzhou, 221116, China. cumt_ckzxg@126.com.; School of Mines and Civil Engineering, Liupanshui Normal University, Liupanshui, China. cumt_ckzxg@126.com.; Guizhou Guineng Investment Co., Ltd, Guiyang, China. cumt_ckzxg@126.com., Wei X; School of Mines, China University of Mining and Technology, Xuzhou, 221116, China. ts21020059a31@cumt.edu.cn., Hongwei J; School of Mines, China University of Mining and Technology, Xuzhou, 221116, China.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2024 Oct 15; Vol. 14 (1), pp. 24080. Date of Electronic Publication: 2024 Oct 15.
DOI: 10.1038/s41598-024-75639-z
Abstrakt: The drilling rate index (DRI) of rocks is important for optimizing drilling operations, as it informs the choice of appropriate methods and equipment, ultimately improving the efficiency of rock excavation projects. This study presents a hybrid machine learning approach to predict the DRI of rocks accurately. By integrating grey wolf optimization with support vector machine (GWO-SVM), random forest (GWO-RF), and extreme gradient boosting (GWO-XGBoost) models, the aim was to enhance predictive accuracy. Among these, the GWO-XGBoost model exhibited superior predictive performance, achieving a coefficient of determination (R²) of 0.999, mean absolute error (MAE) of 0.00043, root mean square error (RMSE) of 1.98017, and severity index (SI) of 0.0350 during training. Testing results confirmed its accuracy with R² of 0.999, MAE of 0.00038, RMSE of 1.80790, and SI of 0.0312. Furthermore, the GWO-XGBoost model outperformed the other models in terms of precision, recall, f1-score, and multi-class confusion matrix results for each DRI class. The GWO-RF model also demonstrated high accuracy, ranking second, while the GWO-SVM model showed comparatively lower performance. This research aims to advance rock excavation practices by providing a highly accurate and reliable tool for DRI prediction. The results highlight the significant potential of the GWO-XGBoost model in improving DRI predictions, offering valuable intuitions and practical applications in the field.
(© 2024. The Author(s).)
Databáze: MEDLINE
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