This study employed a hybridization approach that combines parametric and non-parametric models to predict air over-pressure (AOp) associated with quarry blasting. A simple linear regression model, which is a kind of parametric model, was used to select the most relevant inputs for predicting AOp. Four parametric models, including Chi-square automatic interaction detector (CHAID), artificial neural network (ANN), k-nearest neighbors (KNN), and support vector machine (SVM) were developed using the outputs of a linear model to predict AOp. The models developed were evaluated using five performance indicators, a simple ranking system, and a gains chart. According to the evaluations, ANN and CHAID (both with cumulative ranking = 36) outperformed SVM (cumulative ranking = 15) and KNN (cumulative ranking = 24) to predict AOp. While CHAID (training ranking = 20) performed better than other models in the training phase, ANN (testing ranking = 20) performed better than the other models in the testing phase. In addition, while ANN and CHAID models identified distance as the least important factor for predicting AOp, there was no agreement on the most important factor. Moreover, a comparison between the present study and other studies that used the same dataset showed that, compared to the hybridization of non-parametric models, the hybridization of parametric and non-parametric models potentially results in better accuracy.

这项研究采用了一种混合方法，该方法结合了参数模型和非参数模型来预测与采石场爆破有关的空气超压（AOp）。一个简单的线性回归模型是一种参数模型，用于选择最相关的输入来预测AOp。使用线性模型的输出来预测AOp，开发了四个参数模型，包括卡方自动交互检测器（CHAID），人工神经网络（ANN），k近邻（KNN）和支持向量机（SVM）。使用五个性能指标，一个简单的排名系统和一个收益表来评估开发的模型。根据评估，在预测AOp方面，ANN和CHAID（两者的累积排名= 36）均优于SVM（累积排名= 15）和KNN（累积排名= 24）。虽然CHAID（训练等级= 20）在训练阶段的表现优于其他模型，但ANN（测试等级= 20）在测试阶段的表现优于其他模型。此外，虽然ANN和CHAID模型将距离确定为预测AOp的最不重要因素，但在最重要的因素上并没有达成一致。此外，本研究与使用相同数据集的其他研究之间的比较表明，与非参数模型的混合相比，参数和非参数模型的混合可能会导致更高的准确性。虽然ANN和CHAID模型将距离确定为预测AOp的最不重要因素，但在最重要的因素上并没有达成共识。此外，本研究与使用相同数据集的其他研究之间的比较表明，与非参数模型的混合相比，参数和非参数模型的混合可能会导致更高的准确性。虽然ANN和CHAID模型将距离确定为预测AOp的最不重要因素，但在最重要的因素上并没有达成共识。此外，本研究与使用相同数据集的其他研究之间的比较表明，与非参数模型的混合相比，参数和非参数模型的混合可能会导致更高的准确性。