Application of an Optimized PSO-BP Neural Network to the Assessment and Prediction of Underground Coal Mine Safety Risk Factors

Abstract

Coal has played an important role in the economies of many countries worldwide, which has resulted in increased surface and underground mining in countries with large coal reserves, such as China and the United States. However, coal mining is subject to frequent accidents and predictable risks that have, in some instances, led to the loss of lives, disabilities, equipment damage, etc. The assessment of risk factors in underground mines is therefore considered a commendable initiative. Therefore, this research aimed to develop an efficient model for assessing and predicting safety risk factors in underground mines using existing data from the Xiaonan coal mine. A model for evaluating safety risks in underground coal mines was developed based on the optimized particle swarm optimization-backpropagation (PSO-BP) neural network. The results showed that the PSO-BP neural network model for safety risk assessment in underground coal mines was the most reliable and effective, with MSE, MAPE, and R2 values of 2.0 × 10−4, 4.3, and 0.92, respectively. Therefore, the study proposed the neural network model PSO-BP for underground coal mine safety risk assessment. The results of this study can be adopted by decision-makers for evaluating and predicting risk factors in underground coal mines.