## 1 Introduction
### Practical Applications and Common Findings  
The breadth of studies above shows a consensus that AI/ML techniques can predict blast-induced flyrock with far greater accuracy than older empirical methods. Models such as ANN, SVM, tree ensembles, and hybrids routinely achieve R\({}^{2}\) values in the 0.90-0.99 range, with RMSE on the order of only a few meters to a few tens of meters (depending on dataset scale)--a substantial improvement over classical formulas that often had R\({}^{2}\) values well below 0.5 in comparative tests [27]. Notably, many different model types (from simple fuzzy systems [32] to deep ensembles [29]) have proven successful, suggesting that as long as the major influencing factors are captured, a variety of AI models can map them to flyrock outcomes effectively. Across the literature, there is strong agreement on which input parameters are most influential in flyrock distance. Nearly all studies highlight some measure of explosive charge and rock confinement as key drivers, e.g., powder factor (explosive per rock volume) emerges as a top factor in numerous sensitivity analyses, while maximum charge per delay and stemming length are also repeatedly identified as critical inputs. In contrast, parameters like burden, spacing, or rock density often rank lower in influence, although their effects are not negligible and can be site-specific. The general finding is that flyrock risk is most governed by how much explosive energy is released and how well that energy is confined--which aligns with practical blasting experience. This gives confidence that AI models are picking up meaningful physical relationships, not just statistical coincidences. From a practical standpoint, several research works have moved beyond pure prediction into field implementation and validation. For instance, [PERSON] et al. [23] used their ANN model's recommendations to adjust blasting in an active mine, achieving drastic flyrock reduction (as noted, from 165 m down to 25 m). [PERSON] et al. [28] built a regression-based model and then performed a Monte Carlo simulation to establish a probabilistic safe distance for flyrock; interestingly, the simulated mean flyrock (236.3 m) closely matched the observed mean (238.6 m), giving credence to using such models for defining blast clearance zones. However, they cautioned that models should be used only under conditions similar to those they were developed for and directly applying them elsewhere is not recommended. This caution about generalizability is echoed by many authors. Flyrock is a highly site-specific phenomenon, influenced by local geology and blast practices, so an AI model trained on one mine's data may need retraining or recalibration for another site. The risk of overfitting looms, especially when models boast extremely high R\({}^{2}\) on limited datasets (e.g., an AdaBoost or ensemble ANN that fits one mine's blasts almost perfectly [41; 45]). Without rigorous cross-validation or external testing, such performance might be optimistic. To address this, recent studies increasingly adopt techniques like k-fold cross-validation (as in [37]) and multi-site data fusion (as in 48]) to ensure models generalize. Some works also explicitly exclude less relevant inputs to reduce noise--e.g., using random forest or statistical selection to prune variables--which can improve model robustness when moving to new conditions [36; 49; 50]. Another important practical aspect is data acquisition for model development and use. Traditional methods of measuring flyrock (manual spotters and video analysis from a single camera) can be error-prone (e.g., due to parallax) and dangerous. The adoption of modern monitoring technologies like unmanned aerial vehicles (UAVs) and high-speed videography is making it easier to collect accurate flyrock data for both model training and operational prediction. [PERSON] et al.