# Several Tree-Based Solutions for Predicting Flyrock Distance Due to Mine Blasting
###### Abstract  
Blasting operations involve some undesirable environmental issues that may cause damage to equipment and surrounding areas. One of them, and probably the most important one, is flyrock induced by blasting, where its accurate estimation before the operation is essential to identify the blasting zone's safety zone. This study introduces several tree-based solutions for an accurate prediction of flyrock. This has been done using four techniques, i.e., decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), and adaptive boosting (AdaBoost). The modelling of tree-based techniques was conducted with in-depth knowledge and understanding of their most influential factors. The mentioned factors were designed through the use of several parametric investigations, which can also be utilized in other engineering fields. As a result, all four tree-based models are capable enough for blasting-induced flyrock prediction. However, the most accurate predicted flyrock values were obtained using the AdaBoost technique. Observed and forecasted flyrock by AdaBoost for the training and testing phases received coefficients of determination (R2) of 0.99 and 0.99, respectively, which confirm the power of this technique in estimating flyrock. Additionally, according to the results of the input parameters, the powder factor had the highest influence on flyrock, whereas burden and spacing had the lowest impact on flyrock. mine blasting; environmental issue; flyrock; tree-based techniques; prediction Article  
Several Tree-Based Solutions for Predicting Flyrock Distance Due to Mine Blasting  
[PERSON], A., and A.  
the other hand, such as inappropriate geological features of the rock mass and placing the loosing rock at the top of the free face, may result in flyrock [15]. In terms of the prediction of flyrock, there are three main groups of methods, i.e., empirical, risk-based/statistical and artificial intelligence (AI). One of the most significant empirical formulations for estimating flyrock was presented by [PERSON] et al. [16] on the basis of hole diameter. In another study, [PERSON] [17] proposed a practical relationship using stemming to burden ratio for estimating flyrock. In the study conducted by [PERSON] et al. [18], rock quality designation (RQD) and stemming-to-burden ratio factors were employed for developing an empirical formulation of flyrock. Inadequate empirical equations result due to practical method limitations, a diversity of influencing factors in the flyrock phenomenon, inherent uncertainty, and a complicated blasting operation [19; 20]. As another group of methods in estimating flyrock, the statistical/risk-based approach has been adopted by several scholars. [PERSON] et al. [21] presented a risk-based model for flyrock assessment. In their study, the three main parameters of a factor of safety, threat levels, and flyrock risk were regarded as explaining risk classes for diverse mining situations. According to the presented dynamic danger zone, all factors of safety necessities and production demands are regarded, which makes blasting operations more flexible. [PERSON] et al. [22] developed a multiple regression (MR) for flyrock prediction and then simulated this phenomenon using Monte Carlo (MC). The simulated MC results were very close to the measured flyrock. In another investigation, [PERSON] et al. [23] presented a statistical model for the assessment of flyrock by utilizing the MR approach. They selected burden, hole spacing, length of stemming, and powder factor as input parameters and obtained acceptable accuracy (i.e., a coefficient of determination, R\({}^{2}\), of 0.86). A non-linear MR equation with R\({}^{2}\) values of 0.777 and 0.819 for the training and testing phases, respectively, was introduced by [PERSON] et al. [24]. Risk-based models, probabilistic models, or statistical models are not able to achieve a high level of accuracy in different case studies, as stated by several researchers [24; 25].