Air overpressure (AOp) is a hazardous effect induced by the blasting method in surface mines. Therefore, it needs to be predicted to reduce the potential risk of damage. The aim of this study is to offer an efficient method to predict AOp using a cascaded forward neural network (CFNN) trained by Levenberg–Marquardt (LM) algorithm, called the CFNN-LM model. Additionally, a generalized regression neural network (GRNN) and extreme learning machine (ELM) were employed to demonstrate the accuracy level of the proposed CFNN-LM model. To conduct the CFNN-LM, GRNN, and ELM models, an extensive database, related to four quarry sites in Malaysia, was used including 62 sets of dependent and independent parameters. Next, the performances of the aforementioned models were checked and discussed through statistical criteria and efficient graphical tools. Finally, the results showed the superiority of CFNN-LM (R² = 0.9263 and RMSE = 3.0444) over GRNN (R² = 0.7787 and RMSE = 5.1211) and ELM (R² = 0.6984 and RMSE = 6.2537) models in terms of prediction accuracy. Furthermore, three different regression analysis metrics were used to perform the sensitivity analysis, and according to the obtained results, the maximum charge per delay (\(\beta\) = 0.475, SE = 0.115, t-test = 4.125) was considered as the most influential feature in modeling the AOp.

空气超压（AOp）是露天矿中爆破方法引起的危险影响。因此，需要进行预测以减少潜在的损坏风险。这项研究的目的是提供一种有效的方法，该方法使用由Levenberg-Marquardt（LM）算法（称为CFNN-LM模型）训练的级联前向神经网络（CFNN）来预测AOp。此外，使用广义回归神经网络（GRNN）和极限学习机（ELM）来证明所提出的CFNN-LM模型的准确性。为了进行CFNN-LM，GRNN和ELM模型，使用了与马来西亚四个采石场有关的广泛数据库，其中包括62组相关和独立参数。接下来，通过统计标准和有效的图形工具对上述模型的性能进行了检查和讨论。最后，[R ²  = 0.9263和RMSE = 3.0444）在GRNN（- [R ²  = 0.7787和RMSE = 5.1211）和ELM（- [R ²  = 0.6984和RMSE = 6.2537）模型预测准确性方面。此外，使用了三种不同的回归分析指标进行敏感性分析，根据获得的结果，每个延迟的最大电荷（\（\ beta \）  = 0.475，SE = 0.115，t -test = 4.125）被认为是AOp建模中最有影响力的功能。