Cooperative prediction method of gas emission from mining face based on feature selection and machine learning

Abstract

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.