Application of a hybrid method of machine learning for description and on-line estimation of methane hazard in mine workings

The paper presents application of a hybrid method of methane hazard prediction in exploited mine workings in coal mines. For prediction, the authors used so-called local linear models, the number of which is defined in an adaptive way, and the model of time series prediction ARIMA. The prediction task consists in generating the maximum predicted methane concentration value in a certain time horizon. This forecast is then used to define a methane hazard level by means of a fuzzy system of the Mamdami type. Another important issue covered by the paper is processing of row measurement data to an acceptable form using analytical method and adaptation of the model to changing environmental conditions. The experimental part of the paper presents results of data analysis completed for two longwalls.