基于WOA⁃SVM模型的爆破振动预测研究  

Blasting Vibration Prediction Based on WOA⁃SVM Model

作　　者：王鑫瑀[1] 曹鹏飞 肖一清 徐国权 WANG Xinyu;CAO Pengfei;XIAO Yiqing;XU Guoquan(Hebei Iron&Steel Group Mining Co Ltd,Tangshan 063000,Hebei,China;School of Earth Sciences,East China University of Technology,Nanchang 330000,Jiangxi,China)

机构地区：[1]河北钢铁集团矿业有限公司,河北唐山063000 [2]东华理工大学地球科学学院,江西南昌330000

出　　处：《矿冶工程》2023年第4期48-51,共4页Mining and Metallurgical Engineering

基　　金：国家自然科学基金青年基金(52008080)。

摘　　要：将鲸鱼优化算法(WOA)与支持向量机(SVM)算法结合,建立了一个预测爆破振动的WOA⁃SVM混合模型,使用均方根误差和决定系数作为模型性能评价指标,基于司家营铁矿爆破振动数据,对比了WOA⁃SVM模型、SVM模型、萨道夫斯基模型和USBM模型的预测结果,综合评估结果表明,WOA⁃SVM模型在预测精度方面优于其他模型。With support vector machine(SVM)optimized by whale optimization algorithm(WOA),a WOA⁃SVM hybrid model was established for predicting blasting vibration.Then,with root mean squared error and coefficient of determination as evaluation indices of the model,WOA⁃SVM model,SVM mode,Sadovsky′s model and USBM model were compared based on the data of blasting vibration in Sijiaying Iron Mine.A comprehensive evaluation shows that the WOA⁃SVM model is superior to other models in terms of prediction accuracy.

关 键 词：爆破振动 预测 鲸鱼优化算法 支持向量机 司家营铁矿 

分 类 号：TD235[矿业工程—矿井建设]