In this study, a coupling of generalized linear modeling (GLMNET) and nonlinear neural network modeling with multilayer perceptrons (MLPNN), called GLMNETs–MLPNN modeling, was conducted for predicting air over-pressure (AOp) induced by blasting in open-pit mines. Accordingly, six GLMNET models were developed first. Then, their predictions were bootstrap aggregated as the new predictors, and an optimal MLPNN model was developed based on these new predictors. To prove the improvement of the proposed GLMNETs–MLPNN model, the conventional models, such as GLMNET, support vector machine, MLPNN, random forest, and empirical, were considered and developed based on the same dataset. The results of the proposed model then were compared with that of the conventional models in terms of accurate prediction and modeling. The findings revealed that the bootstrap aggregating of six generalized linear models (i.e., GLMNET models) by a nonlinear model (i.e., MLPNN) could enhance the accuracy in predicting AOp with a root-mean-squared error (RMSE) of 2.266, determination coefficient (R²) of 0.916, and mean squared error (MAE) of 1.718. In contrast, the other stand-alone models provided poorer performances with RMSE of 2.981–4.686, R² of 0.597–0.860, and MAE of 3.156–1.990. Besides, the sensitivity analysis results indicated that burden, stemming, distance, spacing and maximum explosive charge per delay were the most important parameters in predicting AOp.

在这项研究中，进行了广义线性建模（GLMNET）和非线性神经网络建模与多层感知器（MLPNN）的耦合，称为GLMNETs-MLPNN建模，用于预测露天矿爆破引起的空气超压（AOp）。 。因此，首先开发了六个GLMNET模型。然后，将它们的预测自举汇总为新的预测器，并基于这些新的预测器开发最佳的MLPNN模型。为了证明所提出的GLMNETs-MLPNN模型的改进，在相同数据集的基础上考虑并开发了常规模型，例如GLMNET，支持向量机，MLPNN，随机森林和经验模型。然后，在准确的预测和建模方面，将提出的模型的结果与常规模型的结果进行比较。R ²）为0.916，均方误差（MAE）为1.718。相反，其他独立模型的性能较差，RMSE为2.981-4.686，R ²为0.597-0.860，MAE为3.156-1.990。此外，敏感性分析结果表明，负担，茎距，距离，间距和每个延迟的最大炸药装药量是预测AOp的最重要参数。