Research on time series characteristics of the gas drainage evaluation index based on lasso regression

• Published in: Scientific reports Vol. 11; no. 1; p. 20593
• Main Authors: Song, Shuang, Li, Shugang, Zhang, Tianjun, Ma, Li, Zhang, Lei, Pan, Shaobo
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 18.10.2021; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166; 639/4077; Algorithms; Coal mines; Coal mining; Concentration time; Drainage; Drainage effects; Feature selection; Flow rates; Humanities and Social Sciences; multidisciplinary; Science; Science (multidisciplinary); Time series; Wind speed
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-021-00210-z

The evaluation of the coal mine gas drainage effect is affected by many factors, such as flow rate, wind speed, drainage negative pressure, concentration, and temperature. This paper starts from actual coal mine production monitoring data and based on the lasso regression algorithm, features selection of multiple parameters of the preprocessed gas concentration time series to construct gas concentration feature selection based on the algorithm. The three-time smoothing index method is used to fill in the missing values. Aiming at the problem of different dimensions in the gas concentration time series, the MinMaxScaler method is used to normalize the data. The lasso regression algorithm is used to perform feature selection on the multivariable gas concentration time series, and the gas concentration time series selected by the lasso feature and the gas concentration time series without feature selection are input. The performance of the ANN algorithm for gas concentration prediction is compared and analyzed. The optimal α value and L1 norm are selected based on the grid search method to determine the strong explanatory gas concentration time series feature set of the working face, and an experimental comparison of the gas concentration prediction results before and after the lasso feature selection is performed. We verify the effectiveness of the algorithm.