@inproceedings{ba4107d8934b4b6f871aa624897d73ab,
title = "Prediction of Blasting Vibration Intensity by Improved PSO-SVR on Apache Spark Cluster",
abstract = "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.",
keywords = "Big data, Blasting vibration intensity, Prediction algorithm, PSO-SVR, Spark",
author = "Yunlan Wang and Jing Wang and Xingshe Zhou and Tianhai Zhao and Jianhua Gu",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG, part of Springer Nature 2018.; 18th International Conference on Computational Science, ICCS 2018 ; Conference date: 11-06-2018 Through 13-06-2018",
year = "2018",
doi = "10.1007/978-3-319-93701-4_59",
language = "英语",
isbn = "9783319937007",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "748--759",
editor = "Krzhizhanovskaya, {Valeria V.} and Lees, {Michael Harold} and Sloot, {Peter M.} and Jack Dongarra and Yong Shi and Yingjie Tian and Haohuan Fu",
booktitle = "Computational Science – ICCS 2018 - 18th International Conference, Proceedings",
}