Coal seam gas content prediction based on fusion of feature selection and machine learning

Coalbed gas content is an essential parameter for mine gas disaster prevention and CBM exploration and development. In order to improve its prediction accuracy and scientificity of gas content, 35 sets of measured data of coal seam gas content in typical coal mines have been standardized by zero-mean values. Through the complete subset regression method and the random forest feature selection method, the 11 types of parameters that affect the coal seam gas content were selected and combined in different rules and 17 combinations of gas content feature parameters were obtained. Four classic supervised machine learning algorithms, including Gaussian process regression, least squares support vector machine, gradient boosting regression tree, and limit regression machine, were used to predict 17 feature parameter combinations and 68 gas content prediction models were obtained. According to the average judgment coefficient of each machine learning algorithm ≥0.800, 68 kinds of gas content prediction models were preliminarily screened. combined with normalized mean square error≤0.01 and Hill unequal coefficient≤0.01, and 21 optimal prediction models based on the fusion of feature selection and machine learning were obtained. The final prediction sequence was obtained by averaging. The results show that the normalized mean square error of the final prediction sequence is 0.007, the Hill unequal coefficient is 0.005, the determination coefficient is 0.993, the average absolute error is 0.170 m3/t, and the average absolute error is 0.75%. The accuracy evaluation indicators are all In line with the requirements, and the constructed prediction model of multi-method fusion under multi-parameter combination has a wide range of universality and high accuracy.