Cooperative prediction method of coal and gas outburst risk grade based on feature selection and machine learning algorithm

Coal and gas outburst risk prediction is essential to effectively prevent underground coal and gas outburst disasters. In order to improve the scientificity and accuracy of coal and gas outburst risk level prediction, proposed a dynamic prediction model of coal and gas outburst based on multi-algorithm and multivariate analysis. The system selected 51 sets of coal and gas outburst engineering case data as the sample set. Null filling and data standardization were used for preprocessing of the sample data. The 42 prediction models of the coal and gas outburst risk level were built by introducing 6 feature selection methods and 6 supervised machine learning algorithms. The accuracy, confusion matrix, Kappa coefficient and F1 value were used to verify and evaluate the performance of the prediction mode. 8 optimal classification models were determined. Finally, the classification model is used for the prediction of 8 typical coal and gas outburst cases. The results show that the accuracy rate of the 8 optimal classification prediction models is 0. 667-0. 961, the Kappa coefficient is 0. 625-0. 920, and the F1 value is 0. 615-1. The actual case of coal and gas outburst prediction accuracy rate is 100%, and the outburst grade prediction accuracy rate is 87. 5% . The constructed multi-parameter, multi-algorithm, multi-combination, and multi-identification index collaborative prediction system of coal and gas outburst level has high accuracy and a certain degree of universality, which can provide a new way for the prediction of the coal and gas outburst risk level. © 2023 China University of Mining and Technology. All rights reserved.