Study on prediction of blasting vibration velocity based on Grey Wolf Algorithm improved Random Forest Algorithm
In order to improve the prediction accuracy of peak particle velocity during open-pit blasting opera-tions,which is insufficient when using traditional empirical formulas and single bionic algorithms,the Grey Wolf Algo-rithm(GWO)is introduced to optimize 2 hyperparameters,the number and depth of decision trees in the Random Forest Algorithm(RF).This successfully constructs the GWO-RF blasting vibration velocity prediction model.By combin-ing 69 sets of blasting monitoring data from a blasting project,input parameters such as blasthole distance,maximum interval charge,total charge,millisecond delay,number of blastholes,hole spacing,depth,and role spacing are used to compare the prediction of peak vibration velocity using the GWO-RF model and the RF model.The results show that the GWO-RF combined algorithm can consider more practical factors affecting blasting vibration velocity and improves the error rate by 37.83 percentage points compared to the RF;the prediction accuracy of blasting vibration velocity using the GWO-RF combined algorithm reaches 97.72%.This indicates that the GWO successfully optimizes the 2 hyperparameters of decisiontrees in the RF model and demonstrates that the GWO-RF combined algorithm can be used for accurate prediction of blasting vibration velocity in open-pit mining.
open-pit miningblasting vibrationvelocity predictionRandom Forest AlgorithmGrey Wolf Algo-rithm
NETL
NSTL
万方数据
维普
