The attribute quantity of coal mine accident hidden danger was reduced by rough set. The main characteristic attributes were withdrawn; the complexity of predicting system and the computing time was reduced, as well. Then, support vector machine with genetic algorithm (SVMG) is proposed
to forecast the risk of coal mine in China, among which genetic algorithm (GA) is used to determine free parameters of support vector machine. The advantages inherent in rough set, genetic algorithms and support vector machine are incorporated into the hybrid system, making this model highly
applicable to identifying optimal solutions for complex problems. Furthermore, this paper presents evolutionary web-based risk early-warning in coal mine obtained by integrating EFNIM, WWW, and historical risk data to assist in safety management. The experimental results indicate that the
SVMG method can achieve greater accuracy than grey model, artificial neural network under the circumstance of small training data. It was also found that the predictive ability of the SVM outperformed those of some traditional pattern recognition methods for the data set used here.
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