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Performance evaluation of hybrid WOA-XGBoost, GWO-XGBoost and BO-XGBoost models to predict blast-induced ground vibration
Engineering with Computers ( IF 8.7 ) Pub Date : 2021-04-22 , DOI: 10.1007/s00366-021-01393-9
Yingui Qiu , Jian Zhou , Manoj Khandelwal , Haitao Yang , Peixi Yang , Chuanqi Li

Accurate prediction of ground vibration caused by blasting has always been a significant issue in the mining industry. Ground vibration caused by blasting is a harmful phenomenon to nearby buildings and should be prevented. In this regard, a new intelligent method for predicting peak particle velocity (PPV) induced by blasting had been developed. Accordingly, 150 sets of data composed of thirteen uncontrollable and controllable indicators are selected as input dependent variables, and the measured PPV is used as the output target for characterizing blast-induced ground vibration. Also, in order to enhance its predictive accuracy, the gray wolf optimization (GWO), whale optimization algorithm (WOA) and Bayesian optimization algorithm (BO) are applied to fine-tune the hyper-parameters of the extreme gradient boosting (XGBoost) model. According to the root mean squared error (RMSE), determination coefficient (R2), the variance accounted for (VAF), and mean absolute error (MAE), the hybrid models GWO-XGBoost, WOA-XGBoost, and BO-XGBoost were verified. Additionally, XGBoost, CatBoost (CatB), Random Forest, and gradient boosting regression (GBR) were also considered and used to compare the multiple hybrid-XGBoost models that have been developed. The values of RMSE, R2, VAF, and MAE obtained from WOA-XGBoost, GWO-XGBoost, and BO-XGBoost models were equal to (3.0538, 0.9757, 97.68, 2.5032), (3.0954, 0.9751, 97.62, 2.5189), and (3.2409, 0.9727, 97.65, 2.5867), respectively. Findings reveal that compared with other machine learning models, the proposed WOA-XGBoost became the most reliable model. These three optimized hybrid models are superior to the GBR model, CatB model, Random Forest model, and the XGBoost model, confirming the ability of the meta-heuristic algorithm to enhance the performance of the PPV model, which can be helpful for mine planners and engineers using advanced supervised machine learning with metaheuristic algorithms for predicting ground vibration caused by explosions.



中文翻译:

混合WOA-XGBoost,GWO-XGBoost和BO-XGBoost模型的性能评估可预测爆炸引起的地面振动

准确预测爆破引起的地面振动一直是采矿业的重要问题。爆破引起的地面振动是对附近建筑物的有害现象,应予以防止。在这方面,已经开发了一种新的智能方法来预测爆破引起的峰值粒子速度(PPV)。因此,选择了由13个不可控和可控指标组成的150组数据作为输入因变量,并将测得的PPV用作表征爆炸引起的地面振动的输出目标。此外,为了提高其预测精度,还应用了灰狼优化(GWO),鲸鱼优化算法(WOA)和贝叶斯优化算法(BO)来微调极端梯度增强(XGBoost)模型的超参数。R 2),方差占比(VAF)和平均绝对误差(MAE),验证了混合模型GWO-XGBoost,WOA-XGBoost和BO-XGBoost。此外,还考虑了XGBoost,CatBoost(CatB),Random Forest和梯度增强回归(GBR),并将其用于比较已开发的多个混合XGBoost模型。RMSE,R 2的值从WOA-XGBoost,GWO-XGBoost和BO-XGBoost模型获得的,VAF和MAE分别等于(3.0538,0.9757,97.68,2.5032),(3.0954,0.9751,97.62,2.5189)和(3.2409,0.9727,97.65 ,分别为2.5867)。研究结果表明,与其他机器学习模型相比,拟议的WOA-XGBoost成为最可靠的模型。这三个优化的混合模型优于GBR模型,CatB模型,Random Forest模型和XGBoost模型,这证实了元启发式算法增强PPV模型性能的能力,这对矿山计划人员和工程师使用高级监督式机器学习和元启发式算法来预测爆炸引起的地面振动。

更新日期:2021-04-22
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