矿业科学学报2026，Vol.11Issue(2)：286-298,13.DOI:10.19606/j.cnki.jmst.2025084
基于三模混合优化模型RF-WOA-XGBoost的平均爆破块度预测
Prediction of mean blast fragment size based on a tri-model hybrid optimization model RF-WOA-XGBoost
摘要
Abstract
Using historical data from blasting operations of open-pit mines to predict the average rock fragment size is crucial for optimizing blasting parameters.However,existing methods face 3 major challenges:interference from high-dimensional input features,low computational efficiency,and diffi-culties in modeling with sparse datasets.This study therefore proposes a hybrid prediction model that integrates Random Forest(RF),Whale Optimization Algorithm(WOA),and Extreme Gradient Boos-ting(XGBoost).Specifically,the original rock fragment size dataset was subjected to multi-level pre-processing to enhance data quality;RF was employed to evaluate and select 19 input features to reduce dimensionality.WOA was integrated to intelligently optimize the hyperparameters of the prediction mod-el;XGBoost was used to model the small-sample rock fragment size dataset.Comparative experiments showed that this model exhibited better prediction performance with an R2 value of 0.93,outperforming other control group models.Additionally,the clear modeling process design further enhanced the oper-ability and engineering application of the prediction model.关键词
平均爆破块度预测/随机森林/鲸鱼优化算法/极端梯度提升模型/数据驱动Key words
mean fragmentation size prediction/RF/WOA/XGBoost/data-driven分类
矿业与冶金引用本文复制引用
徐子璎,孙阳,孙金山,周晓光,李昱捷..基于三模混合优化模型RF-WOA-XGBoost的平均爆破块度预测[J].矿业科学学报,2026,11(2):286-298,13.基金项目
湖北省教育厅科学研究计划青年人才项目(Q20234407) （Q20234407）
