Prediction of mean blast fragment size based on a tri-model hybrid optimization model RF-WOA-XGBoost

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 difficulties 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 Boosting (XGBoost). Specifically, the original rock fragment size dataset was subjected to multi-level preprocessing 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 model; 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 operability and engineering application of the prediction model.