Abstract: In order to forecast effectively coal-and-gas outburst in coal-mine,a new method for coal-and-gas outburst forecast based on CCPSO (complete chaotic particle swarm optimization) and SVM (support vector machine) was presented. With multi-fractal dimension spectrum of gas emission amount dynamic time series in the front of work-face in coal-mine being feature index, the forecasting model was constructed by using SVM. The parameters vector of the proposed model was selected and optimized by CCPSO and the criteria of CERM (classification error rate and TSSM (test sample set minimization). The experimental results show that the proposed method is effective and provides a new approach for forecasting coal-and-gas outburst in coal-mine.

Key words: Coal-and-gas outburst, Forecast, Support vector machine, Complete chaotic particle swarm optimization, Multi-fractal dimension spectrum