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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.

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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。

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王鑫瑀（1983—），男，吉林晖春人，硕士，高级工程师，主要从事采矿工程方面的研究。

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王鑫瑀（1983—），男，吉林晖春人，硕士，高级工程师，主要从事采矿工程方面的研究。

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