An ensemble technique namely gradient boosted tree (GBTs) and several optimized neural network models were hybridized to predict peak particle velocity (PPV) caused by quarry blasting. The GBT was employed for choosing the most important input parameters on PPV results. Therefore, this model selected five input variables, comprising maximum charge per delay, distance, powder factor, and sub-drilling, and RQD. Once the input assortment was performed, five neural network models, including a typical artificial neural network (ANN) and ANNs with weight optimization (forward, backward, particle swarm optimization, PSO, and evolutionary), were implemented utilizing the inputs picked by the GBT. These models were assessed by several performance criteria, including the “correlation coefficient”, “root mean square error”, “variance accounted for”, “a20-index”, and a simple ranking system, as well as optimized weights. The results of hybridization showed that ANN-PSO model outperformed other models in terms of system error and accuracy. Altogether, this study's findings implied that consolidating the ensemble machine learning technique and optimized ANN models, particularly PSO could result in perfect and straightforward to understand predictions of PPV caused by quarry blasting.

一种集成技术，即梯度提升树 (GBT) 和几个优化的神经网络模型被混合在一起，以预测由采石场爆破引起的峰值粒子速度 (PPV)。GBT 用于选择 PPV 结果中最重要的输入参数。因此，该模型选择了五个输入变量，包括每次延迟的最大电荷、距离、粉末系数、子钻孔和 RQD。一旦输入分类被执行，五个神经网络模型，包括一个典型的人工神经网络 (ANN) 和具有权重优化的 ANNs（前向、后向、粒子群优化、PSO 和进化），利用 GBT 选择的输入被实施. 这些模型通过几个性能标准进行评估，包括“相关系数”、“均方根误差”、“方差解释”、“a20-index”，一个简单的排名系统，以及优化的权重。杂交结果表明，ANN-PSO 模型在系统误差和精度方面优于其他模型。总而言之，这项研究的结果表明，整合集成机器学习技术和优化的 ANN 模型，尤其是 PSO 可以完美而直接地理解采石场爆破引起的 PPV 预测。