## 1 Introduction
### Practical Applications and Common Findings
[51] performed a comprehensive bibliometric review and noted that conventional means of locating flyrock fragments suffer from biases and errors, and they demonstrated a field trial where UAV-mounted cameras captured flyrock trajectories more reliably. The UAV-based method showed clear advantages in capturing the full flight of rock fragments, which were then analyzed with motion analysis software, and a soft computing model was used to predict the recorded flyrock distances [51]. Similarly, [PERSON] et al. [52] used high-speed cameras onsite (including UAV-mounted) to record flyrock from blasts at five mines, generating a rich dataset of launch velocities and distances. They then trained several ML models, deploying the best one (an extremely randomized trees regressor) in a prototype software tool to simulate flyrock trajectories for new blasts. This integration of AI models into user-friendly interfaces allows engineers to input planned blast parameters and visualize the predicted flyrock hazard zone, greatly aiding in blast design and risk mitigation. More generally, UAV photogrammetry and remote sensing techniques are increasingly utilized in mining, as they can provide rapid, high-resolution mapping of blast sites and measure outcomes like fragmentation and throw with centimeter-level precision. Studies have shown UAV-based monitoring to be efficient, removing the need for personnel in hazardous areas and improving data quality. For example, [PERSON] et al. [53; 54] used UAVs to continuously monitor open-pit blasts, capturing detailed images for fragmentation analysis and flyrock observation, which offered insights for process improvement that traditional manual monitoring could miss. The proliferation of such technologies, as reviewed by [PERSON] and [PERSON] [55] and [PERSON] and [PERSON] [56], underscores that UAVs have become an excellent tool in mining for tasks ranging from surveying to blast monitoring. In the context of flyrock prediction, better data acquisition means more reliable models and the possibility of near-real-time model updating and validation during blasting operations. In conclusion, the application of AI and machine learning to flyrock prediction in open-pit blasting has yielded marked improvements in predictive accuracy and practical risk management. Diverse models--ANNs, SVMs, fuzzy systems, tree ensembles, and numerous hybrids--have each demonstrated high accuracy (with R\({}^{2}\) often in the high 0.9 range and errors reduced by 50-80% compared to older methods) in various case studies [27; 29; 33; 45]. Hybrid models that combine techniques (ANN + GA, ANFIS + PSO, optimized SVR, etc.) tend to achieve the best performance, reflecting a common finding that no single method is universally best, but combining algorithms can harness their complementary strengths for superior results [37; 38; 41; 42; 57]. A recurring theme across studies is the identification of crucial parameters: in almost all models, powder factor (or specific charge) and stemming length emerge as primary controls on flyrock distance, whereas parameters like burden, spacing, or rock quality may have secondary effects. This consistency builds confidence in the physical relevance of the models. On the other hand, researchers consistently warn about limitations: models are usually data-driven and thus valid only within the domain of the training data. For instance, [PERSON] et al. [28] explicitly state that their site-specific model should not be directly applied to other conditions without recalibration. To improve generalizability, recent works have embraced cross-validation, larger multi-site datasets, and input reduction to avoid overfitting [37; 38]. Another concern is data representativeness--since flyrock events are stochastic and sometimes rare (extreme flyrock incidents might not occur in every dataset), ensuring the model has seen enough \"bad cases\" is important for reliable hazard prediction.