## 3 Results and Discussions
### XG Boost Regression  
To avoid modeling complexity, two stopping methods, maximum tree depth and n rounds, were selected in the current XG Boost Regression Model. Choosing significant numbers for maximum tree depth and n rounds would result in excessive development of the tree and an over-fitting issue. Therefore, to avoid this problem, the maximum tree depth was set to 1-3 and n rounds to 50, 100, and 150. A trial-and-error approach was used with a range of two values to get the best combination of these two parameters. The dataset was randomly sorted into training (80%) and testing (20%). For the training set, K-fold cross-validation was used to determine the optimum model parameters. The training set was divided into 10 parts of about equal size for 10-fold cross-validation, of which 9 parts were used for training and 1 part utilized for validation. This method was repeated 10 times repeatedly, and the average of these values was used to get the predicted forecast accuracy. The initial prediction began by taking into account the mean of the dependent variables, MFS and PPV, from the dataset. The residual values from the previous prediction points were then computed. Furthermore, utilizing (80%) of the data, the model was trained to build an XG Boost tree using Equations (1)-(3). As illustrated in Figures 18-21 the tree  
Figure 16: Performance of MAPE, RMSE, and R\({}^{2}\) on testing data. Figure 17: Fragmentation and PPV results comparison between measures models and predicted. divided the data into two portions. The similarity score and gain were calculated by taking the average of the two nearest leaves and moving the residue to the leaf with the highest score and gain. To avoid model complexity, the learning rate and maximum depth were set to 1.0 and 3. After acquiring predictions (residuals) from model 1, all data points were run through model 1 to obtain h1(x) and the F1(x) prediction and residuals of model 2. Similarly, the technique for obtaining h2(x) for model 3 was carried out. Figure 19: Tree 2 fragmentation. Figure 18: Tree 1 fragmentation. The final empirical formulas for predicting fragmentation and peak particle velocity are provided in Equations (10) and (11), which were generated from the trained model using Figure 18. The flow chart created utilizing the weight-age of the Se/Be ratio, Total explosive quantity (TE), Firing Pattern (FP), and joint angle degree (JAD) in terms of squared errors, samples, and prediction values from the trees shown in Figures 18-21. Maximum Charge per Delay was not considered since the output iterations did not balance  
Figure 21: Tree 2 PPV. Figure 20: Tree 1 PPV. accuracy in anticipating fragmentation and PPV as the numbers ranged from 110 to 210 kg with little fluctuation, as shown in Table 2.  
\[\text{Fragmentation}=0.77+0.1(\text{Tree 1})+0.1(\text{Tree 2}) \tag{10}\]  
\[\text{PeakParticleVelocity}=3.60+0.1(\text{Tree 1})+0.1(\text{Tree 2}) \tag{11}\]  
where tree 1 and 2 are final prediction values of models. Using Figure 22, one can predict the simultaneous results of fragmentation and PPV. The process depicted in Figure 18 can be used to run the predetermined values of Se/Be, TE, FP, and JAD based on the geo-mechanical condition of the rock mass. Both fragmentation and PPV tree models produced TE as the root node. If the predetermined value of TE is less than or equal to 7181, the pattern continued to the left (Yes); otherwise, it proceeded to the right (No). Furthermore, the final prediction value of tree 1 was chosen based on the comparison values of pre-decided blast parameters and actual parameters derived by the models shown in Figure 17:0 Tree 2 of fragmentation and PPV trees 1 and 2 were predicted using similar step. Figures 23 and 24 demonstrate the relationship between the characteristics Se/Be, FP, TE, and JAD and target both fragmentation and peak particle velocity. The PYTHON Seabborn module was used to build correlation heat maps. The association between the  
Figure 17:1 Tree reading process to opt prediction value.