基于三模混合优化模型RF-WOA-XGBoost的平均爆破块度预测OA

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.