# Several Tree-Based Solutions for Predicting Flyrock Distance Due to Mine Blasting
###### Abstract
Considering the limitations of the presented methods, implementing novel approaches such as AI and soft computing (SC) techniques for estimating flyrock seems essential and has been considered by several researchers. [PERSON] et al. [14] presented a fuzzy interface system (FIS) model for forecasting flyrock phenomena in the Gol-E-Gohar iron mine, Iran. A Mamdani fuzzy model was implemented in this model considering a database with 490 datasets, and this model was able to receive a high level of accuracy for flyrock estimation. A backpropagation neural network model was proposed by [PERSON] et al. [26] for forecasting flyrock, considering controllable factors of blasting as model inputs. According to [PERSON] et al. [26], the powder factor has the greatest impact on the flyrock. In another study, [PERSON] et al. [27] showed adequate proficiency of both the FIS and neural network in predicting flyrock induced by blasting. Two intelligent flyrock equations, i.e., gene expression programming (GEP) and genetic programming, were proposed by [PERSON] et al. [28] with a high degree of accuracy. A regression-based technique (i.e., support vector machine) was introduced by [PERSON] and [PERSON] [29] to predict flyrock. They reported a close value between the predicted and measured flyrock (R\({}^{2}\) = 0.948). Hybrid AI and SC techniques which are a combination of metaheuristic algorithms and other base models, such as neural networks and extreme learning machines, were also developed for flyrock prediction [30; 31; 32; 33; 4]. It is obvious that AI and SC techniques are the best categories for flyrock forecasting among the available methodologies in terms of accuracy. It is also important to note that these techniques have been widely employed and suggested in the literature for solving engineering problems [35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70  
soils using Adaboost and enhanced tree methods. For this purpose, they gathered 440 data samples of soil features and achieved acceptable results. A regression tree technique was used for rock strength assessment in the study conducted by [PERSON] et al. [61]. In another application of tree-based models, the gradient-boosted tree (GBT) technique was used by [PERSON] et al. [62] to forecast pile friction-bearing capacity. The testing phase showed enough efficiency for the GBT model to provide predictions of pile friction-bearing capacity. These techniques were also successful in slope stability classification in an interesting investigation by [PERSON] et al. [63]. They suggested the AdaBoost approach with an accuracy of 0.93 for slope stability classification. In light of the above discussion, several tree-based techniques, i.e., DT, RF, XGBoost, and AdaBoost, are used by the authors of this study to evaluate their powers in predicting flyrock induced by blasting. The best model will be chosen after comparing their performance predictions. Below is a plan for the rest of this paper:  
The background of tree-based techniques will be described in Section 2. In Section 3, a description of Sungun Copper Mine as the case study will be given. In addition, a statistical overview of the dataset will be proposed in the same section. The modelling procedure will be described in detail in Section 4 with four sub-sections. In Section 5, the performance of tree-based models is assessed and discussed. Finally, Section 6 will discuss the concluding remarks of this study.