Study on Risk Classification of Goaf Based on RS-SVM

¹ School of Civil Engineering, Hefei University of Technology, hefei anhui 230009, China
² Laboratoire de Génie Civil et géo-Environnement, Université de Lille, 5900 Lille, France
³ State Engineering Laboratory of Highway Maintenance Technology, Changsha University of Science and Technology, Changsha, China

Abstract

According to the uncertainty and concealment of the risk of goaf, a risk classification model of goaf is constructed based on rough set (RS) knowledge and support vector machine (SVM) theory. In this paper, based on statistical analysis and measured data, nine parameters including mining method, empty area excavation depth, goaf height, maximum exposed area of empty area, maximum exposure height, maximum exposure span, pillar condition, empty volume and treatment rate are selected as the main influencing factors. The RS theory is used to reduce the sample, and SVM is compiled by Matlab. The one-to-one method is used to construct the binary classifier to realize the multi-class classification algorithm of goaf. Finally, a SVM model for evaluating the risk level of the goaf is obtained. The research shows that: based on RS theory, SVM has a good effect on the hazard classification of the goaf iron ore mine, and the difference with the actual situation is 13.3%. The research results have certain theoretical significance and guiding role for the safe mining of an iron mine in Eastern China.