After each blasting operation in surface mines, undesirable environmental impacts, such as air-blast (AB) and ground vibration, are inevitable. Therefore, minimizing and controlling these impacts are crucial in order to reduce environmental problems. This study presents new and practical advanced machine learning methods for AB prediction using 62 datasets gathered from four quarry sites in Malaysia. The developed models were constructed based on the boosted regression tree (BRT) and least-squares support vector machine (LSSVM), improved with three metaheuristic algorithms: the gray wolf optimizer (GWO), genetic algorithm (GA), and artificial bee colony (ABC). Six hybrid models, namely BRT–GA, BRT–ABC, BRT–GWO, LSSVM–GA, LSSVM–ABC, and LSSVM–GWO models, were developed and their performances were evaluated using metrics such as R-squared correlation and other methods like the Taylor diagram and quantile–quantile plots. To provide a better assessment of the models' performances, the dataset were categorized into training and testing parts. The results demonstrated that, among the six hybrid models, the LSSVM–GWO model provided the highest efficiency in the testing part while the BRT–GWO model had the best accuracy but the performance of BRT–GWO was the best in the training part. In other words, the BRT hybrid models had the best performance in training, and LSSVM hybrid models in the testing part. The results indicate the effectiveness of combining GWO with LSSVM and BRT models to predict AB.

露天矿的每次爆破作业后，不可避免地会产生不良的环境影响，例如空气冲击波 (AB) 和地面振动。因此，最大限度地减少和控制这些影响对于减少环境问题至关重要。这项研究使用从马来西亚四个采石场收集的 62 个数据集，提出了用于 AB 预测的新的、实用的先进机器学习方法。所开发的模型基于提升回归树（BRT）和最小二乘支持向量机（LSSVM）构建，并使用三种元启发式算法进行改进：灰狼优化器（GWO）、遗传算法（GA）和人工蜂群（ ABC）。开发了六种混合模型，即 BRT-GA、BRT-ABC、BRT-GWO、LSSVM-GA、LSSVM-ABC 和 LSSVM-GWO 模型，并使用R平方相关等指标和其他方法（如泰勒图和分位数-分位数图。为了更好地评估模型的性能，数据集被分为训练和测试部分。结果表明，在六种混合模型中，LSSVM-GWO模型在测试部分提供了最高的效率，而BRT-GWO模型的准确性最好，但BRT-GWO在训练部分的性能最好。换句话说，BRT混合模型在训练部分具有最好的性能，而LSSVM混合模型在测试部分具有最好的性能。结果表明，GWO 与 LSSVM 和 BRT 模型相结合来预测 AB 的有效性。