# Application of Bayesian Neural Network (BNN) for the Prediction of Blast-Induced Ground Vibration
## 6 Conclusions  
Ground vibration has a negative impact not only on the environment of the surrounding area, but also on the rock itself, and the surrounding human population. The release of energy that occurs during an explosion is what causes ground vibration; the intensity of the vibration is determined by several parameters, including the amount of explosive employed, the type of rock that is blasted, and the distance from the source of the blast. The PPV is the most important principal base parameter practice for understanding, evaluating, and predicting ground vibration, in terms of the vibration velocity. This is because the PPV is a measure of the most forward particles, in terms of the transverse (T), vertical (V), and longitudinal (L) velocities. This study aims to predict the blast-induced ground vibration of the Mikurahana quarry, using a Bayesian neural network (BNN) and four machine learning techniques, including random forest, gradient boosting, decision tree, and k-neighbors.  
What follows is a few of the conclusions that can be derived from the research that was presented:  
1. The proposed model was developed using eight input parameters (MIC, SD, distance, elevation, blast longitudinal, blast latitude, measured latitude, and measured longitude) and one hundred recorded blasting datasets. The data are divided into two parts: the training data (80% of the total dataset) and the test data (20% of the total dataset).
2. The assessment of one model over the other was tested by using different performance evaluation metrics, such as mean squared error (MSE), root-mean-square error (RMSE), and Pearson's correlation coefficient (R). Additionally, SHAP (Shapley additive explanation) was performed, after the evaluation of the model, to understand the feature importance of the model and avoid the issue of the black box.
3. The results obtained from the BNN model are compared with four machine learning regression analyses, and we found that the performances of the BNN model are superior to those of the traditional models, and the BNN model outperformed other models, with a lower error (RMSE = 0.17, MSE = 0.03, and R = 0.94). In consideration of the number of factors and the assessment criteria, these findings can be regarded as  
Figure 13: Feature importance of the model. Color scale: pink colors correspond to high importance of a feature, while blue colors denote low importance.  
remarkably accurate. For the estimation of the PPV parameters, four machine learning algorithms were evaluated. The findings indicated that these relationships have weak PPV estimation capabilities. Due to its nonlinear structure, great flexibility, and low error, a BNN is much more capable of estimating the PPV than other models.
4. By adopting the BNN technique, we can predict the PPV before a blast occurs. Adjustments may be made to the blast design so that blast disturbances are reduced, and more explosive energy will be utilized effectively and efficiently.  
Conceptualization, Y.F.; methodology, Y.F.; software, Y.F.; validation, Y.F.; formal analysis, Y.F.; investigation, Y.F.; resources, H.I. and T.A.; data curation, H.I.; writing--original draft preparation, Y.F.; writing--review and editing, Y.F.; visualization, Y.K. and T.A.; supervision, H.T. and T.A.; project administration, Y.K.; funding acquisition, Y.K. and H.I. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
We thank the anonymous reviewers and members of the editorial team for their comments and contributions.  
The authors declare