Web-Based Risk Early-Warning in Coal Mine Using Support Vector Machine and Genetic Algorithm

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Abstract:

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 The main characteristic attributes were withdrawn; the complexity of predicting system and the computing time was reduced, as well.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.

Keywords: GENETIC ALGORITHM (GA); RISK EARLY-WARNING; ROUGH SET (RS); SUPPORT VECTOR MACHINE (SVM)

Document Type: Research Article

DOI: http://dx.doi.org/10.1166/asl.2012.2273

Publication date: March 1, 2012

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  • ADVANCED SCIENCE LETTERS is an international peer-reviewed journal with a very wide-ranging coverage, consolidates research activities in all areas of (1) Physical Sciences, (2) Biological Sciences, (3) Mathematical Sciences, (4) Engineering, (5) Computer and Information Sciences, and (6) Geosciences to publish original short communications, full research papers and timely brief (mini) reviews with authors photo and biography encompassing the basic and applied research and current developments in educational aspects of these scientific areas.
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