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Gold Science and Technology ›› 2023, Vol. 31 ›› Issue (4): 624-634.doi: 10.11872/j.issn.1005-2518.2023.04.026

• Mining Technology and Mine Management • Previous Articles     Next Articles

PPV Prediction Model Based on Random Forest Optimized by SMA Algorithm

Hongwei DENG(),Liang LUO   

  1. School of Resources and Safety Engineering，Central South University，Changsha 410083，Hunan，China
  • Received:2023-02-20 Revised:2023-04-19 Online:2023-08-30 Published:2023-09-20

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Abstract:

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.

Key words: open blasting, blasting vibration velocity peak, random forest algorithm, slime mould algorithm, prediction accuracy

CLC Number: 

  • TD253

Fig.1

Schematic diagram of random forest"

Table 1

Hyper-parameters optimized in random forest algotithm"

超参数含义范围
ntree树的个数0~300
mtry最小叶子点数0~20

Fig.2

SMA-RF model flow chart"

Table 2

Measured data of blasting vibration"

序号Qmax/kgr/mH/mW/m实测值/（cm·s-1）
1580.820005.00.763581
2600.8310756.00.489885
3300.82821505.00.007130
4260.860155.01.893658
5280.84682394.50.692248
??????
23260.8110155.01.191707

Fig.3

Distributions of training set and testing set for SMA-RF model"

Fig.4

Iteration situation of fitness value in SMA-RF prediction model"

Fig.5

Predicted PPV for training and testing sets by SMA-RF prediction models"

Table 3

Result evaluation of SMA-RF prediction model with different input parameters"

参数组合训练集总分
MAE得分R2得分RMSE得分MEDEA得分
Q-r1.389310.929011.929910.947214
Q-H-r0.876740.969441.250340.5444416
Q-W-r1.246420.956421.497021.105528
Q-W-H-r1.045630.963731.358230.7775312
参数组合测试集总分
MAE得分R2得分RMSE得分MEDEA得分
Q-r1.618330.996242.023041.2771213
Q-H-r1.694210.917612.155221.732415
Q-W-r1.650620.980132.221011.124539
Q-W-H-r1.395840.963622.070230.7131413

Table 4

Comparison of SMA-RF prediction model scores with different input parameters"

参数组合训练集得分测试集得分总分排名
Q-r413173
Q-H-r165212
Q-W-r89173
Q-W-H-r1213251

Table 5

Evaluation of RF prediction model results"

评价指标训练集测试集
MAE1.46051.6951
R20.96750.9045
RMSE2.02012.1923
MEDEA1.06561.6955

Table 6

Six groups of common empirical formulas at home and abroad"

经验方法公式
Amb-HendPPV=Kr/Qmax1/3-B
CMRIPPV=n+Kr/Qmax1/2-1
GeneralPPV=KQmaxAr-B
Indian StandardPPV=Kr/Qmax2/3B
Lang-KihlPPV=K(Qmax/r3/4)B
USBMPPV=Kr/Qmax1/2-B

Table 7

Constant terms and fitting evaluation indexes of six groups of empirical formulas"

经验公式参数评价指标
KBAnMAER2RMSEMEDEA
USBM13.10270.7479--3.09440.01955.68261.6959
Lang-Kihl0.28671.9959--2.80630.10314.33411.4054
General9.3569e-50.53922.1427-2.29200.26493.78870.1357
Amb-Hend24.93670.6897--3.17930.00794.56761.8497
Indian Standard6.10200.7806--3.02190.03414.55701.7112
CMRIP12.9205--1.13303.15710.00944.58261.6516

Fig.6

PPV prediction of six groups of empirical formulas"

Fig.7

Average MSE decrease of four input parameters"

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