Research on Prediction Model of Gas Emission Based on Lasso Penalty Regression Algorithm

Abstract

Researches show that the amount of mine gas emission is influenced by many factors, including the buried depth of coal seams, coal thickness, gas content, CH₄ concentration, daily output, coal seam distance, permeability, volatile yield, air volume, etc. Its high-dimensional characteristics could easily lead to dimension disaster. In order to eliminate the collinearity of attributes and avoid the over-fitting of functions, Lasso algorithm is used to reduce the dimension of variables. After low-redundancy feature subset is obtained, the best performance model is selected by 10-fold cross-validation method. Finally, the gas emission is predicted and analyzed based on public data from coal mine. The results show that the prediction model based on Lasso has higher accuracy and better generalization performance than principal component analysis prediction model,and the accurate prediction of gas emission can be realized more effectively.