A wavelet-based robust relevance vector machine based on sensor data scheduling control for modeling mine gas gushing forecasting on virtual environment

Abstract

It is wellknown that mine gas gushing forecasting is very significant to ensure the safety of mining. A wavelet-based robust relevance vector machine based on sensor data scheduling control for modeling mine gas gushing forecasting is presented in the paper. Morlet wavelet function can be used as the kernel function of robust relevance vector machine. Mean percentage error has been used to measure the performance of the proposed method in this study. As the mean prediction error of mine gas gushing of the WRRVM model is less than 1.5%, and the mean prediction error of mine gas gushing of the RVM model is more than 2.5%, it can be seen that the prediction accuracy for mine gas gushing of the WRRVM model is better than that of the RVM model. © 2013 Wang Ting et al.