PPV Prediction Model Based on Random Forest Optimized by SMA Algorithm
Received date: 2023-02-20
Revised date: 2023-04-19
Online published: 2023-09-20
The vibration caused by blasting is likely to cause instability and failure of facilities such as underground roadways，high and steep slopes in mining areas or ground buildings under dynamic action.Therefore，it is particularly important to predict the intensity of blasting vibration.The accurate prediction of peak particle velocity（PPV） is the premise of effectively controlling the vibration hazard of blasting engineering，but the current empirical formula for predicting the peak particle velocity is not accurate enough.Machine learning has obvious advantages in solving the problem of nonlinear relationship.In order to improve the prediction accuracy of the PPV prediction model，this study proposes to optimize the number of trees and the minimum number of leaf points in the random forest （RF）by slime mould algorithm （SMA） ，which overcomes the inability to obtain the optimal hyperparameters by using a single RF algorithm.Based on a dataset of 23 samples with four input parameters （minimum resistance line-r，height difference-H，maximum segment dose-Qmax，horizontal distance-W） and one output parameter（PPV） collected in an open-pit blasting engineering example，the combination of four parameters of these four parameters （Qmax-H-W-r、Qmax-H-r、Qmax-W-r、Qmax-r） was used as the input parameters in the RF algorithm，and then MAE，RMSE，MEDEA and R2 evaluate the prediction effect of the SMA-RF model for four different input parameters to determine the optimal combination of parameters.In this model，the fitness function in SMA is defined as the root mean square error of the predicted value to enhance the robustness of the RF model.Then，the performance of SMA-RF model and unoptimized RF model and six empirical formulas commonly used in China and abroad were compared.The results show that the SMA-RF model has better prediction accuracy than the RF model，and the SMA-RF model has significantly better prediction effect than the six empirical formulas.In addition，Qmax-H-W-r can train the optimal SMA-RF model in the combination of four parameters，so it is recommended to be used to predict PPV in engineering practice.
Hongwei DENG , Liang LUO . PPV Prediction Model Based on Random Forest Optimized by SMA Algorithm[J]. Gold Science and Technology, 2023 , 31(4) : 624 -634 . DOI: 10.11872/j.issn.1005-2518.2023.04.026
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