A Rigorous Evaluation of Metaheuristically Optimized Machine Learning Models for Blast-Induced Flyrock Prediction
TL;DR
The results showed that appropriate cross-validation and metaheuristic hyperparameter optimization improved predictive performance, with the ET–STHVO model achieving the best overall results.
Abstract
Blast-induced flyrock is one of the most critical hazards in surface mining and quarrying, posing significant risks to occupational safety, nearby structures, and the environment. Accurate prediction of flyrock distance is therefore essential for safe blast design and effective risk management. In this study, Random Forest (RF), Extra Trees (ET), and Support Vector Regression (SVR) models were developed to predict flyrock distance, and their hyperparameters were optimized using the Secretary Bird Optimization Algorithm (SBOA) and the Spider-Tailed Horned Viper Optimizer (STHVO). Prior to optimization, six cross-validation strategies were compared using GridSearchCV to identify the most appropriate strategy for each model. The final models were evaluated on an independent test dataset using the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), variance accounted for (VAF), and Nash–Sutcliffe efficiency (NSE). Model interpretability was investigated using SHapley Additive exPlanations (SHAP). The results showed that appropriate cross-validation and metaheuristic hyperparameter optimization improved predictive performance, with the ET–STHVO model achieving the best overall results. SHAP analysis identified B/D, PF, H/B, RBS, and U/B as the most influential predictors. The proposed framework provides an accurate, robust, and interpretable decision-support tool for safer blasting operations.