The research was led by Mohammad Matin Rouhani of Amirkabir University of Technology in Tehran, together with Mahdi Hasanipanah of Duy Tan University, Xin Yin of Wuhan University of Science and Technology, Hesam Dehghani of Hamedan University of Technology, and Mohammad Rezaei of the University of Kurdistan. Their goal was ambitious: to build a predictive system that could generalize reliably to new blasting events at a major copper mine, while giving engineers a transparent view of the physical factors that matter most. Traditional empirical equations for flyrock, the team notes, often fall short because they cannot capture the complex, nonlinear interplay between explosive energy, rock properties, and blast geometry.

To train their models, the researchers assembled a dataset of 252 blasting events recorded at the Sungun Copper Mine in northwestern Iran, one of the country’s largest open-pit operations. Each event was characterized by a set of controllable blast design parameters, including the powder factor, which describes the amount of explosive energy delivered per unit of rock; the burden, the distance between boreholes and the free face of the rock; the spacing between holes; the stemming length, the inert material packed atop the explosive charge; the bench height; and the diameter of the drill holes. The output variable was the measured flyrock distance, the maximum throw of rock fragments beyond the blast zone.

At the heart of the framework sits XGBoost, or extreme gradient boosting, a machine learning algorithm that builds a strong predictive model from an ensemble of decision trees, each new tree correcting the errors of its predecessors. XGBoost has become a workhorse of applied machine learning because of its speed and accuracy, but its performance depends heavily on the tuning of internal settings known as hyperparameters, such as tree depth, learning rate, and the number of boosting rounds. Poorly tuned hyperparameters can leave substantial predictive power on the table, or worse, cause a model to memorize training data rather than learn generalizable patterns.

The team’s key innovation was to pair XGBoost with five different optimization algorithms that automatically search for the best hyperparameter configuration. These included Bayesian optimization, a statistically guided search method that models the relationship between hyperparameters and performance; the geometric mean optimization algorithm, a newer population-based technique; the osprey optimization algorithm, inspired by the hunting behavior of the fish-eating raptor; reptile search optimization, a nature-inspired metaheuristic mimicking reptilian hunting strategies; and the Archimedes optimization algorithm, which draws on principles of buoyancy and physics. Each hybrid model was trained and tested on the Sungun dataset, and their performances were compared using multiple statistical indicators, radar plots, and Taylor diagrams, which visualize how closely each model’s predictions match observed values in terms of correlation and variability.

The results were striking. The GMO-XGBoost model, which combines XGBoost with geometric mean optimization, delivered the best generalization performance, achieving the highest prediction accuracy on unseen testing data. This matters because a model that excels only on data it has already seen is of little practical use; engineers need forecasts they can trust for future blasts. Interestingly, the AOA-XGBoost variant, built on the Archimedes optimization algorithm, showed superior performance during the training phase, illustrating a common tension in machine learning between fitting known data and generalizing to new situations. The fact that different optimizers excelled at different stages underscores why the team ran the full comparison rather than assuming a single best approach.

But the researchers went a step further than raw prediction. To open the black box of their best-performing models, they applied Shapley additive explanations, or SHAP analysis, a technique borrowed from cooperative game theory that quantifies each input variable’s contribution to every individual prediction. The analysis revealed a clear hierarchy of influence: the powder factor, burden, and stemming length emerged as the most powerful controls on flyrock behavior, with powder factor showing the strongest effect. This finding aligns with physical intuition, since the powder factor directly governs the explosive energy available to launch rock fragments, while burden and stemming determine how that energy is contained and directed.

The practical implications for mine operators are significant. With an interpretable model in hand, blast engineers can run what-if scenarios before a single hole is drilled, adjusting the powder factor or stemming design to keep predicted flyrock within safe exclusion zones. That translates into fewer evacuations, less equipment damage, reduced liability, and a smaller environmental footprint around the mine. The framework also supports sustainable mine planning more broadly, because blasting that throws rock unpredictably can disturb surrounding habitats, contaminate nearby land with debris, and erode community trust. By making flyrock a quantified, decision-oriented variable, the study moves hazard assessment from reactive investigation to proactive design.

The work also reflects a broader shift in the geosciences toward explainable artificial intelligence. For years, machine learning models in mining and rock engineering have been criticized for offering high accuracy without insight, leaving practitioners unable to justify safety-critical decisions. By embedding SHAP analysis directly into the modeling pipeline, the Sungun study demonstrates that accuracy and interpretability need not be competing goals. The authors describe this combination of optimized hybrid machine learning and explainable AI as the study’s central contribution, positioning it as a template for other geo-environmental hazards such as ground vibration, air blast, and back-break, where similar hybrid frameworks are already gaining traction.

Limitations remain, as they do in any data-driven study. The models were trained on data from a single mine, and while the GMO-XGBoost variant generalized well within that setting, transferring the framework to sites with different rock types, explosives, or drilling practices would require retraining and validation. The researchers indicate that the underlying data will be shared on reasonable request, which should help other groups test the approach. Still, the message of the study is clear: with 252 real blasts, five competing optimizers, and a transparent view of what drives the danger, flyrock prediction has entered a new era, one in which the rocks thrown by an explosion can be forecast before the fuse is ever lit.

Subject of Research: Intelligent prediction of blast-induced flyrock distance in surface mining using optimized hybrid XGBoost machine learning models and explainable AI

Article Title: Intelligent Assessment of Blast-Induced Flyrock as a Geo-Environmental Hazard Using Optimized Hybrid XGBoost Models

Article References: Rouhani, M. M., Hasanipanah, M., Yin, X., Dehghani, H., & Rezaei, M. (2026). Intelligent Assessment of Blast-Induced Flyrock as a Geo-Environmental Hazard Using Optimized Hybrid XGBoost Models. Natural Resources Research. https://doi.org/10.1007/s11053-026-10757-1

Image Credits: AI Generated

DOI: 10.1007/s11053-026-10757-1

Keywords: flyrock, surface mining, XGBoost, machine learning, blasting hazard, Sungun Copper Mine, hyperparameter optimization, explainable AI, SHAP analysis, geo-environmental hazard, sustainable blasting, powder factor