In this study, two innovative techniques were introduced, including data enrichment and optimization, with the aim of significantly improving the accuracy of air over-pressure (AOP) prediction models in mine blasting. Firstly, the Extra Trees algorithm was applied to enrich the collected dataset with the goal of enhancing the understanding of the predictive models for AOP prediction. Then, a neural network model with two hidden layers (ANN) was designed to predict AOP using both the original and enriched datasets. Secondly, to further enhance the accuracy of the ANN model, a novel optimization algorithm based on a random walk strategy and the grey wolf optimization algorithm (RWGWO) was employed to optimize the weights of the ANN model. This optimized model, referred to as the RWGWO–ANN model, was developed and evaluated for predicting AOP using both the original and enriched datasets. To comprehensively assess the impact of data enrichment and the proposed RWGWO-ANN model, three other optimization algorithms—particle swarm optimization (PSO), fruit-fly optimization algorithm (FOA), and single-based genetic algorithm (SGA)—were also applied to optimize the ANN model for AOP prediction. These models were named PSO–ANN, FOA–ANN, and SGA–ANN, respectively. The tenfold cross-validation procedure was applied and repeated three times to ensure the objectivity and consistency of the models. Additionally, conventional ANN and the United States Bureau of Mines empirical model were developed for comparison, serving similar purposes to evaluate the efficiency of the optimization algorithms employed in this study. To demonstrate the advantages of the proposed method and models, a dataset comprising 312 blasting events and six input parameters at the Coc Sau open-pit coal mine in Vietnam was gathered and analyzed. These parameters included burden, spacing, rock hardness, powder factor, monitoring distance, and maximum explosive charge per delay. An additional input variable—Extra Trees—was introduced, making the total number of input variables seven in the enriched dataset. The proposed hybrid model, along with others, was developed based on both the original and enriched datasets. The results revealed that the Extra Trees algorithm is robust and effectively enriches the raw dataset, enhancing the understanding of predictive models and providing improved accuracy. Sensitivity analysis results also highlighted the robust contribution of the Extra Trees variable in the enriched dataset. Compared to the original dataset, the performance of AOP predictive models was improved by 7–24% using the enriched dataset enriched by the Extra Trees algorithm. Furthermore, the findings indicated that the RWGWO–ANN model exhibited the highest accuracy in predicting AOP in this study, achieving an accuracy of 96.2%. This marked a 16–20% improvement over the accuracy of the conventional ANN model.

在这项研究中，引入了两项创新技术，包括数据丰富和优化，旨在显着提高矿井爆破中空气超压（AOP）预测模型的准确性。首先，应用 Extra Trees 算法来丰富收集的数据集，目的是增强对 AOP 预测的预测模型的理解。然后，设计了具有两个隐藏层 (ANN) 的神经网络模型，以使用原始数据集和丰富数据集来预测 AOP。其次，为了进一步提高ANN模型的精度，采用基于随机游走策略和灰狼优化算法（RWWWO）的新型优化算法来优化ANN模型的权重。这种优化模型（称为 RWGWO-ANN 模型）是为使用原始数据集和丰富数据集预测 AOP 而开发和评估的。为了全面评估数据丰富和所提出的 RWGWO-ANN 模型的影响，还应用了其他三种优化算法——粒子群优化（PSO）、果蝇优化算法（FOA）和基于单一的遗传算法（SGA）优化 AOP 预测的 ANN 模型。这些模型分别命名为 PSO-ANN、FOA-ANN 和 SGA-ANN。应用十倍交叉验证程序并重复三次，以确保模型的客观性和一致性。此外，还开发了传统的人工神经网络和美国矿业局的经验模型进行比较，以类似的目的来评估本研究中采用的优化算法的效率。为了证明所提出的方法和模型的优点，收集并分析了越南 Coc Sau 露天煤矿的 312 次爆破事件和 6 个输入参数的数据集。这些参数包括载荷、间距、岩石硬度、粉末系数、监测距离和每次延迟的最大炸药装药量。引入了额外的输入变量“Extra Trees”，使丰富数据集中的输入变量总数达到 7 个。所提出的混合模型以及其他模型是基于原始数据集和丰富数据集开发的。结果表明，Extra Trees 算法非常稳健，可以有效丰富原始数据集，增强对预测模型的理解并提高准确性。敏感性分析结果还强调了 Extra Trees 变量在丰富数据集中的强大贡献。与原始数据集相比，使用 Extra Trees 算法丰富的丰富数据集，AOP 预测模型的性能提高了 7-24%。此外，研究结果表明，RWGWO-ANN 模型在本研究中预测 AOP 的准确性最高，达到 96.2% 的准确性。这标志着传统 ANN 模型的准确性提高了 16-20%。