A novel artificial intelligent model for predicting air overpressure using brain inspired emotional neural network

Abstract

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.

Keywords

• Air overpressure
• Artificial intelligence
• Emotional neural network
• Blasting
• Mining