Causes selection and risk level prediction of coal mine gas explosion accident

Abstract

In order to accurately predict the risk level of coal mine gas explosion accidents, based on the feature vector in line with the actual situation, the particle swarm optimization probabilistic neural network (RWPSO-PNN) is improved to realize the prediction model of gas explosion risk level. Firstly, the cause of coal mine gas explosion accident is extracted by Chinese word segmentation, and the input feature vector of the model is selected by grey correlation analysis (GRA). Aiming at the problem of low recognition rate caused by smoothing factor in probabilistic neural network (PNN), RWPSO-PNN is proposed to adjust the smoothing factor adaptively. Finally, RWPSO-PNN is analyzed and compared with extreme learning machine algorithm, BP neural network and support vector machine algorithm. The results show that the prediction accuracy of RWPSO-PNN is 90 %, and the average absolute error is 0.133, which is obviously better than the comparison algorithm.

Keywords

• coal mine gas risk level prediction
• accident causes
• probabilistic neural network
• improved particle swarm optimization
• grey relational analysis