Collaborative prediction model of gas emission quantity was built by feature selection and supervised machine learning algorithm to improve the scientific and accurate prediction of gas emission quantity in the mining face. The collaborative prediction model was screened by precision evaluation index. Samples were pretreated by data standardization, and 20 characteristic parameter combinations for gas emission quantity prediction were determined through 4 kinds of feature selection methods. A total of 160 collaborative prediction models of gas emission quantity were constructed by using 8 kinds of classical supervised machine learning algorithm and 20 characteristic parameter combinations. Determination coefficient, normalized mean square error, mean absolute percentage error range, Hill coefficient, mean absolute error, and the mean relative error indicators were used to verify and evaluate the performance of the collaborative forecasting model. As such, the high prediction accuracy of three kinds of machine learning algorithms and seven kinds of characteristic parameter combinations were screened out, and seven optimized collaborative forecasting models were finally determined. Results show that the judgement coefficients, normalized mean square error, mean absolute percentage error, and Hill inequality coefficient of the 7 optimized collaborative prediction models are 0.969–0.999, 0.001–0.050, 0.004–0.057, and 0.002–0.037, respectively. The determination coefficient of the final prediction sequence, the normalized mean square error, the mean absolute percentage error, the Hill inequality coefficient, the absolute error, and the mean relative error are 0.998%, 0.003%, 0.022%, 0.010%, 0.080%, and 2.200%, respectively. The multi-parameter, multi-algorithm, multi-combination, and multi-judgement index prediction model has high accuracy and certain universality that can provide a new idea for the accurate prediction of gas emission quantity.

通过特征选择和有监督的机器学习算法建立瓦斯排放量协同预测模型，提高对工作面瓦斯排放量的科学准确预测。协同预测模型采用精度评价指标进行筛选。对样本进行数据标准化预处理，通过4种特征选择方法确定20个用于气体排放量预测的特征参数组合。采用8种经典的监督机器学习算法和20个特征参数组合，共构建了160个气体排放量协同预测模型。确定系数、归一化均方误差、平均绝对百分比误差范围、希尔系数、平均绝对误差、并使用平均相对误差指标来验证和评估协同预测模型的性能。由此筛选出3种机器学习算法和7种特征参数组合的高预测精度，最终确定了7种优化的协同预测模型。结果表明，7个优化协同预测模型的判断系数、归一化均方误差、平均绝对百分比误差和希尔不等式系数分别为0.969-0.999、0.001-0.050、0.004-0.057和0.002-0.037。最终预测序列的决定系数、归一化均方误差、平均绝对百分比误差、希尔不等式系数、绝对误差、平均相对误差为0.998%，分别为 0.003%、0.022%、0.010%、0.080% 和 2.200%。多参数、多算法、多组合、多判断指标预测模型具有较高的准确性和一定的普适性，可为气体排放量的准确预测提供新思路。