关键词: 随机森林, 因子分析, 煤与瓦斯突出, 强度预测

Abstract: Coal and gas outburst is one of the principal dynamic disasters in coal mine underground mining. Aimed at solving the prediction problem of coal and gas outburst risk level, improve the outburst prediction accuracy, this paper selects 9 factors as evaluation index which affects the coal and gas outburst classification prediction, including maximum principal stress, gas pressure, gas content, roof lithology, distance from the fracture of roof, coal seam thickness, mining depth, the absolute gas emission and relative gas emission, and do the factor analysis to the most related evaluation index, extract the public factor, with random forest algorithm training, establish the coal and gas outburst prediction model based on factor analysis and random forest. Based on 19 groups of coal and gas outburst data measured by coal mine as the training sample data sets for model training, 5 groups of data as test data for model testing, do the prediction of coal and gas outburst, meanwhile compared with other prediction models, the test results verify the random forest algorithm in coal and gas outburst prediction has higher accuracy.

Key words: random forest, factor analysis, coal and gas outburst, strength prediction