Blasting is the live wire of mining and its operations, with air overpressure (AOp) recognised as an end product of blasting. AOp is known to be one of the most important environmental hazards of mining. Further research in this area of mining is required to help improve on safety of the working environment. Review of previous studies has shown that many empirical and artificial intelligence (AI) methods have been proposed as a forecasting model. As an alternative to the previous methods, this study proposes a new class of advanced artificial neural network known as brain inspired emotional neural network (BI-ENN) to predict AOp. The proposed BI-ENN approach is compared with two classical AOp predictors (generalised predictor and McKenzie formula) and three established AI methods of backpropagation neural network (BPNN), group method of data handling (GMDH), and support vector machine (SVM). From the analysis of the results, BI-ENN is the best by achieving the least RMSE, MAPE, NRMSE and highest R, VAF and PI values of 1.0941, 0.8339%, 0.1243%, 0.8249, 68.0512% and 1.2367 respectively and thus can be used for monitoring and controlling AOp.

爆破是采矿及其作业的带电工具，空气超压（AOp）被认为是爆破的最终产品。众所周知，AOp是采矿的最重要的环境危害之一。需要对该采矿领域进行进一步的研究，以帮助改善工作环境的安全性。对先前研究的回顾表明，已经提出了许多经验和人工智能（AI）方法作为预测模型。作为以前方法的替代方法，本研究提出了一类称为脑启发情感神经网络（BI-ENN）的先进的人工神经网络来预测AOp。将拟议的BI-ENN方法与两个经典的AOp预测变量（广义预测变量和McKenzie公式）和三种已建立的反向传播神经网络AI方法进行比较，数据处理的分组方法（GMDH）和支持向量机（SVM）。从结果分析来看，BI-ENN最好，因为它的RMSE，MAPE，NRMSE最少，而最高R，VAF和PI值分别为1.0941、0.8339％，0.1243％，0.8249、68.0512％和1.2367，因此可用于监视和控制AOp。