Prediction of Blasting Vibration Intensity by Improved PSO-SVR on Apache Spark Cluster

  • Yunlan Wang
  • Jing Wang
  • Xingshe Zhou
  • Tianhai Zhao
  • Jianhua Gu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10861)


In order to predict blasting vibration intensity accurately, support vector machine regression (SVR) was adopted to predict blasting vibration velocity, vibration frequency and vibration duration. The mutation operation of genetic algorithm (GA) is used to avoid the local optimal solution of particle swarm optimization (PSO). The improved PSO algorithm is used to search for the best parameters of SVR model. In the experiments, the improved PSO-SVR algorithm was realized on the Apache Spark platform. The execution time and prediction accuracy of the sadovski method, the traditional SVR algorithm, the neural network (NN) algorithm and the improved PSO-SVR algorithm were compared. The results show that the improved PSO-SVR algorithm on Spark is feasible and efficient, and the SVR model can predict the blasting vibration intensity more accurately than other methods.


Blasting vibration intensity Prediction algorithm PSO-SVR Spark Big data 


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Yunlan Wang
    • 1
  • Jing Wang
    • 1
  • Xingshe Zhou
    • 1
  • Tianhai Zhao
    • 1
  • Jianhua Gu
    • 1
  1. 1.School of Computer Science, Center for High Performance ComputingNorthwestern Polytechnical UniversityXi’anChina

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