Recognition of goaf risk based on support vector machines method
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Abstract:
Complicated non-linear relationship is existed between the grade of goaf risk and its influence factors. In order to classify the grade of goaf risk, support vector machines classification method is studied. In this paper, rock structures, geological structure, rock compressive strength, elasticity modulus, shape of goaf, ore body angle, engage in collapse, volume of goaf are treated as main influencing factors. A 1-V-1 classification algorithm is proposed based on SVM, as well as SVM models are built for classification in Matlab. Taking one underground mine as an example, the paper uses the SVM models to recognize goaf risk and compares the result with that of BP neural network prediction. It shows that the classification of SVM method is more accurate than that of neural network method, and there is a high agreement with the survey results. SVM is a feasible method on goaf risk evaluation.
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Supported by National High-tech R&D Program of China (863 Program), Discipline Construction Project of Xi’an University of Architecture and Technology(E09003) and Research Fund for Young Teachers of PuNai Education Scholarship.
