Research Of Coal And Gas Outburst Prediction Based On Data Mining Technology

Coal mine safe production and miners'security are threatened by coal and gas outburst, and accurate prediction of the danger of coal and gas outburst and taking preventive measures timely are particularly important. The gas concentration is abnormity in the lane when the coal and gas outburst happens, the dynamic process of the coal and gas outburst is reflected by the continuity gas concentration, so characteristics of the gas concentration is used as the critical value of coal and gas outburst prediction. The gas concentration is detectable in the coal mine where the coal mine gas monitoring system is established, large amounts of the gas concentration data are stored in the system by strongly database technology, wealthy data resources is provided for making out the relation between the discipline of gas concentration and the danger of coal and gas outburst. But the coal mine gas monitoring system lacks of the function of further analysis, and could alarm only when the gas over limit, do not have the early warning capabilities for the potential hazards of coal and gas outburst, so causes the waste of large amounts of data. Data mining which is widely used in economics is introduced for this problem and tries to excavate the relation between abnormal concentration and coal and gas outburst, the prediction model is established, the characteristics of the gas concentration could be classified, the danger of coal and gas outburst is forecast, and the guidance to decision-making is provided for mine management.Task of various stages is defined by establishment of data mining model. In data preparation phase of data mining process, some of gas concentration time series within ten hours before coal and gas outburst happening and some of gas concentration time series within ten hours before coal and gas outburst does not happen are made as samples get from large amounts of historical gas concentration data, the time series is modeled by ARIMA method, the parameters of model are used as feature vectors. In data mining phase, the machine learning methods of support vector machine is used as mining algorithm, the prediction model of coal and gas outburst based on support vector machine is trained by samples feature vectors. Finally, this prediction model is compared with the prediction model based on BP artificial neural network by simulation experiment, the prediction model based on support vector machine having the higher prediction performance is proved by experimental results, the danger of coal and gas outburst could be predicted by the prediction model based on support vector machine established by data mining.