Research on prediction of coal and gas outburst risk based on TIWTD-CNSA

Abstract

When processing the current coal and gas outburst risk index data, it is observed that the data sequence does not have intuitive regularity, and the acquired data signals are generally mixed with different degrees of noise, which hinders data processing and prediction. To reduce the influence of noise signal on prediction, it is proposed to use Translation-invariant Wavelet Threshold Denoising (TIWTD) to process signal data, and the result shows that the noise signal in the original data is significantly reduced. On this basis, a Compact Negative Selection Algorithm (CNSA) is introduced to detect abnormal signals exceeding the critical value in the data and make predictions. After training the model with the gas hazard index feature subset collected during the excavation process of No. 11192 mining face in a coal mine from Guizhou province in China, input the processed indicator data into the model for prediction. The results show that the model can effectively identify the anomalies in the data and accurately predict the risk of coal and gas outburst, the TIWTD-CNSA model can be used to guide engineering practice.