Blast-induced ground vibration (GV) is a hazardous phenomenon in open-pit mines, and it has unquestionable effects, such as slope instability, deformation of structures, and changing the flow direction of groundwater. Therefore, many studies in recent years have focused on the accurate prediction and control of GV in open-pit mines. In this study, three intelligent hybrid models were examined for predicting GV based on different nature-inspired optimization algorithms and deep neural networks. Accordingly, a deep neural network (DNN) was developed for predicting GV under the enhancement of deep learning techniques. Subsequently, aiming at improving the accuracy and reducing the error of the DNN model in terms of the prediction of blast-induced GVs, three optimization algorithms based on the behaviors of whale, Harris hawks, and particle swarm in nature (abbreviated as WOA, HHOA, and PSOA, respectively) were considered and applied, namely HHOA–DNN, WOA–DNN, and PSOA–DNN, respectively. The results were then compared with those of the conventional DNN model through various performance indices; 229 blasting events in an open-pit coal mine in Vietnam were processed for this aim. Finally, it was found that the proposed intelligent hybrid models outperform the DNN model with deep learning techniques, although it is a state-of-the-art model that has been recommended and claimed by previous researchers. In particular, HHOA, WOA, and PSOA (with global optimization) further improved the accuracy of the DNN model by 1–2%. Of those, the HHOA–DNN model provided the highest performance with a mean-squared-error of 2.361, root mean squared error of 1.537, mean absolute percentage error of 0.123, variance accounted for of 93.015, and coefficient determination of 0.930 on the testing dataset. The findings also revealed that the explosive charge per blast, monitoring distance, and time delay per each blasting group are necessary parameters for predicting GV.

爆破引起的地面振动（GV）是露天矿山的一种危险现象，其具有无可置疑的影响，如边坡失稳、结构变形、改变地下水流向等。因此，近年来的许多研究都集中在露天矿GV的准确预测和控制上。在这项研究中，基于不同的自然启发优化算法和深度神经网络，研究了三种智能混合模型来预测 GV。因此，在深度学习技术的增强下，开发了用于预测 GV 的深度神经网络 (DNN)。随后，为了提高 DNN 模型在预测爆炸诱发 GVs 的准确性和减少误差，三种基于鲸鱼、Harris hawks 和 hawks 行为的优化算法，考虑并应用了自然界中的粒子群（分别缩写为 WOA、HHOA 和 PSOA），即 HHOA-DNN、WOA-DNN 和 PSOA-DNN。然后通过各种性能指标将结果与传统 DNN 模型的结果进行比较；为此，对越南露天煤矿的 229 次爆破事件进行了处理。最后，发现所提出的智能混合模型在深度学习技术方面优于 DNN 模型，尽管它是先前研究人员推荐和声称的最先进模型。特别是 HHOA、WOA 和 PSOA（具有全局优化）进一步将 DNN 模型的准确率提高了 1-2%。其中，HHOA-DNN 模型提供了最高的性能，均方误差为 2.361，均方根误差为 1.537，平均绝对百分比误差为 0.123，方差为 93.015，测试数据集的系数确定为 0.930。研究结果还表明，每次爆炸的炸药装药量、监测距离和每个爆破组的时间延迟是预测 GV 的必要参数。