# Toward a State-of-the-Art of Fly-Rock Prediction Technology in Open-Pit Mines Using EANNs Model  
[PERSON]  
1 Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam; [EMAIL_ADDRESS]  
[PERSON]  
2 Department of Surface Mining, Mining Faculty, Hanoi University of Mining and Geology, 18 Vien st., Duc Thang ward, Bac Tu Liem dist., Hanoi 100000, Vietnam; [EMAIL_ADDRESS]  
Turing Nguyen-Thoi  
early based on experimental techniques (e.g., [7; 8; 9]). However, due to improper energy use factors of explosives, such as improper stemming, excessive powder factor, inadequate burden, unfavorable geological conditions, to name a few, the uncontrolled rocks are still propelled beyond the security area [10; 11; 12]. Therefore, empirical techniques were unable to explain the relationship between fly-rock and influencing factors adequately, and were uncontrollable in predicting whether blast-induced fly-rock was unavoidable.  
To overcome the limitations of empirical methods, data-mining techniques were studied and applied by many scientists for predicting fly-rock in blasting operations. Its effectiveness has been proven not only in fly-rock forecasts but also in general technical issues [11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31]. For estimating the distance of fly-rock, [PERSON] et al. [32] used a branch of artificial intelligence (AI), namely ANN (artificial neural network), based on the blasting parameters (i.e., powder factor, the diameter of borehole, blast ability index, stemming length, and a charge per delay). Finally, the charge per delay was defined as the most critical factor for reducing fly-rock. Similar works using the ANN technique for predicting fly-rock were also conducted by [PERSON] et al. [33], and [PERSON] et al. [34]. Another machine-learning algorithm of AI, namely SVM (support vector machine), was also applied to predict the fly-rock distance by [35], [PERSON], [PERSON] [36]. ANFIS (adaptive neuro-fuzzy inference system) was also an AI technique that has been successfully applied by [PERSON] et al. [37], for predicting the fly-rock distance with an excellent result.  
However, conventional AI techniques do not seem to satisfy scientists. Therefore, [PERSON] et al. [38] utilized an optimization algorithm, namely PSO (particle swarm optimization), for developing a robust equation for fly-rock prediction. Another meta-heuristic algorithm (i.e., differential evaluation algorithm-DE) was also used by [PERSON] and [PERSON] [39] to combine with the dimensional analysis algorithm (DA) for estimating fly-rock distance. Its performance was confirmed by a variety of empirical techniques in their study. Based on the actions of fireflies, [PERSON] et al. [40] also optimized blasting parameters to minimize the distance of fly-rock, using the firefly algorithm (FA). The genetic algorithm (GA) was also included to optimize the recurrent fuzzy neural network (RFNN), for predicting the fly-rock distance [41]. Under the optimal power of GA, an optimal RFNN model was established with a promising result in their study.  
An overview of the related works indicated that many AI techniques had been developed to predict the fly-rock distance. However, they have not been confirmed in different areas with different geological conditions. Furthermore, new methods/models are always the targets of research by scientists aiming to improve the accuracy of the previous forecast models, minimizing the risks posed by fly-rock. Therefore, we proposed a state-of-the-art technology of fly-rock prediction based on ANN models and their robust combination, namely EANNs (ensemble of ANN models).