Gas Concentration Monitoring Prewarning Based on Adaptive Prediction and Feature Extraction

Abstract

This study presents a reliable method for predicting gas concentration and implementing prewarning analysis. Gas monitoring data are decomposed into intrinsic mode functions (IMFs) with different time scales by using empirical mode decomposition (EMD), which represents the intrinsic features of gas concentration on different time scales. The prediction accuracy is evaluated by the prediction effectiveness, and the IMF phase space parameters and the Gaussian process regression (GPR) hyperparameters are dynamically adjusted to achieve adaptive prediction. Combined with singular value decomposition (SVD) to extract the intrinsic features of gas monitoring data, a prediction and prewarning model is established. The case study shows that the prediction accuracy of the adaptive model is significantly higher than that of direct GPR prediction and that it solves the problem of low prediction accuracy at mutational points in gas concentration time series. The degree of influence of the production process on the variation in gas concentration is quantitatively determined to improve the reliability of prewarning applications.