SVM Based Prediction of Spontaneous Combustion in Coal Seam

Spontaneous Combustion in Coal Seam (SCCS) is seriously threatening coal mine safety. A novel approach to predict SCCS by using Support Vector Machine (SVM) is present. The SVM is based on statistical learing theory with a simple structure and good generation properties. The basic SVM principle was firstly reviewed. Then, the kernel function was choiced, and the model parameters were optimized with cross-validation and grid-research method. Finally,a comparision of the preformance of SVM with radial basis function neural networks (RBF-NN) was carried out.The experimental results show that the highest classification accuracy (100%) is obtained for the SVM model, and the SVM-based model makes predictions much more accurate than RBF-NN model does when the samples are limited. Consequently, a properly trained SVM classification model can be a strong predictor for SCCS prediction procedure.