关键词: 瓦斯涌出量, 预测, SVM-LMD, 采煤工作面

Abstract: In this paper, the method that using LMD (Local Mean Decomposition) to obtain production function components for SVM (Support Vector Machine) modeling was proposed, which was applied to forecast the gas emission volume in coalface. First, the historical data of gas emission volume were resolved by LMD to get the production function components, i.e. PF components. Then, extrapolation forecasting of each PF component was carried out by using SVM function fitting method,respectively. In addition, the forecasting results were reconstructed, and the forecasted theoretical values of gas emission volume were finally obtained. From the case study in one mine, the forecasting accuracy of the method proposed in this paper is higher than conventional SVM methods, and the established forecasting model of coalface gas emission based on this method has better rationality and reliability. Therefore, with the acquisition of production function components and small sample forecasting by SVM, the physical mechanisms and laws in data can be fully exploited, which accords well with the physical mechanism that using data themselves to get their interaction. This method provides a basis for improving the forecasting accuracy of gas emission volume.

Key words: gas emission volume, forecasting, SVM-LMD, coalface