Enhanced Prediction Model for Blast-Induced Air Over-Pressure in Open-Pit Mines Using Data Enrichment and Random Walk-Based Grey Wolf Optimization-Two-Layer ANN Model

In this study, two innovative techniques were introduced, including data enrichment and optimization, with the aim of significantly improving the accuracy of air over-pressure (AOP) prediction models in mine blasting. Firstly, the Extra Trees algorithm was applied to enrich the collected dataset with the goal of enhancing the understanding of the predictive models for AOP prediction. Then, a neural network model with two hidden layers (ANN) was designed to predict AOP using both the original and enriched datasets. Secondly, to further enhance the accuracy of the ANN model, a novel optimization algorithm based on a random walk strategy and the grey wolf optimization algorithm (RWGWO) was employed to optimize the weights of the ANN model. This optimized model, referred to as the RWGWO-ANN model, was developed and evaluated for predicting AOP using both the original and enriched datasets. To comprehensively assess the impact of data enrichment and the proposed RWGWO-ANN model, three other optimization algorithms-particle swarm optimization (PSO), fruit-fly optimization algorithm (FOA), and single-based genetic algorithm (SGA)-were also applied to optimize the ANN model for AOP prediction. These models were named PSO-ANN, FOA-ANN, and SGA-ANN, respectively. The tenfold cross-validation procedure was applied and repeated three times to ensure the objectivity and consistency of the models. Additionally, conventional ANN and the United States Bureau of Mines empirical model were developed for comparison, serving similar purposes to evaluate the efficiency of the optimization algorithms employed in this study. To demonstrate the advantages of the proposed method and models, a dataset comprising 312 blasting events and six input parameters at the Coc Sau open-pit coal mine in Vietnam was gathered and analyzed. These parameters included burden, spacing, rock hardness, powder factor, monitoring distance, and maximum explosive charge per delay. An additional input variable-Extra Trees-was introduced, making the total number of input variables seven in the enriched dataset. The proposed hybrid model, along with others, was developed based on both the original and enriched datasets. The results revealed that the Extra Trees algorithm is robust and effectively enriches the raw dataset, enhancing the understanding of predictive models and providing improved accuracy. Sensitivity analysis results also highlighted the robust contribution of the Extra Trees variable in the enriched dataset. Compared to the original dataset, the performance of AOP predictive models was improved by 7-24% using the enriched dataset enriched by the Extra Trees algorithm. Furthermore, the findings indicated that the RWGWO-ANN model exhibited the highest accuracy in predicting AOP in this study, achieving an accuracy of 96.2%. This marked a 16-20% improvement over the accuracy of the conventional ANN model.