Blasting has been widely recognized as an economical and viable method in geo-engineering projects. However, the induced ground vibration in terms of peak particle velocity (PPV) potentially can damage the nearby environment and inhabitants. Therefore, more accurate prediction of the PPV can lead to reduce undesirable and hazardous effects of blasting. With the increase in the computational power, wide variety of predictive PPV models using numerical tools and data mining approaches have been presented. In this paper, the optimum predictive PPV model was specified using generalized feedforward neural network (GFFN) structure integrated with a novel automated intelligent setting parameter approach. Subsequently, two new optimized hybrid models using GFFN incorporated with firefly and imperialist competitive metaheuristic algorithms (FMA and ICA) were developed and applied on 78 monitored events in Alvand–Qoly mine, Iran. According to analyzed metrics, the predictability level of hybrid GFFN-FMA dedicated 6.67% and 20% progress than GFFN-ICA and optimum GFFN. The pursued performance using precision–recall curves and ranked accuracy criteria also exhibited superior improvement in GFFN-FMA. Sensitivity analyses implied on the importance of the distance and burden as the most and least effective factors on predicted induced PPV in the study area.

爆破已被广泛认为是地球工程项目中一种经济可行的方法。然而，就峰值粒子速度 (PPV) 而言，诱发的地面振动可能会破坏附近的环境和居民。因此，更准确地预测 PPV 可以减少爆破的不良和危险影响。随着计算能力的增加，已经提出了使用数值工具和数据挖掘方法的各种预测 PPV 模型。在本文中，使用与新型自动智能设置参数方法相结合的广义前馈神经网络 (GFFN) 结构指定了最佳预测 PPV 模型。随后，使用 GFFN 结合萤火虫和帝国主义竞争性元启发式算法（FMA 和 ICA）开发了两种新的优化混合模型，并将其应用于伊朗 Alvand-Qoly 矿的 78 个监测事件。根据分析的指标，混合 GFFN-FMA 的可预测性水平比 GFFN-ICA 和最佳 GFFN 分别提高了 6.67% 和 20%。使用精度-召回曲线和排序精度标准所追求的性能在 GFFN-FMA 中也表现出卓越的改进。敏感性分析表明距离和负担的重要性是研究区域预测诱导 PPV 最有效和最无效的因素。使用精度-召回曲线和排序精度标准所追求的性能在 GFFN-FMA 中也表现出卓越的改进。敏感性分析表明距离和负担的重要性是研究区域预测诱导 PPV 最有效和最无效的因素。使用精度-召回曲线和排序精度标准所追求的性能在 GFFN-FMA 中也表现出卓越的改进。敏感性分析表明距离和负担的重要性是研究区域预测诱导 PPV 最有效和最无效的因素。