Gas outburst prediction model using rough set and support vector machine
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Gas outburst prediction model using rough set and support vector machine Liu Haibo1 · Dong Yujie2 · Wang Fuzhong1 Received: 14 May 2020 / Revised: 7 September 2020 / Accepted: 27 September 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract This paper is concerned with the problem of gas outburst prediction in coal mine working face. To predict the gas outburst accurately, this paper uses the rough set theory (RS) and support vector machine (SVM) to establish the prediction model. Firstly, based on the analysis of influencing factors of gas outburst, 10 factors including coal thickness variations, geological structures and gas change are selected as the influencing factors. By using the attribute reduction algorithm to eliminate redundant information, the gas outburst influencing factors as input to the prediction model are reduced from 10 to 6 in decision table. Secondly, by applying the particle swarm optimization (PSO) algorithm to optimize penalty parameter and kernel function of SVM and improve the generalization performance of model, the nonlinear relationship between main influencing factors and intensity of gas outburst is established. Finally, 60 sets of data of Jiulishan Coal Mine in Henan are used as training and testing samples to verify the proposed prediction model, and the discriminant results is compared with that of RBF model and SVM model. The results show that the prediction accuracy of the proposed model is 93%, which is improved compared with the other two models. The RS-PSOSVM model can reduce data redundancy, avoid the model to fall into the local extremum, and can predict the risk level of gas outburst effectively. Keywords Gas outburst · Rough set theory · Support vector machine · Particle swarm optimization · Prediction
1 Introduction China is one of the countries with the most serious coal and gas outburst disasters in the world. Coal mine gas outburst poses a serious threat to the safety of coal mines and the safety of people’s lives and property. Gas outburst prediction is an important part of the current "four in one" comprehensive outburst prevention system. Therefore, it is of great significance to carry out prediction research on outburst for gas accident prevention and mine safety production [1, 2]. Coal and gas outburst is a kind of complicated dynamic phenomenon in the process of coal mining, which can eject a large amount of crushed coal and gas into stope space and roadway in a short time. The prediction of coal mine gas outburst is the basis of gas prevention work, and has * Liu Haibo [email protected] 1
School of Electric Engineering and Automation, Henan Polytechnic University, Jiaozuo 454000, China
College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo 454000, China
2
attracted considerable attention in recent decades. The main methods are neural network method [3, 4], rough set theory [5], fuzzy theory [6], case-based reasoning method [7], support vector machine [8–11], bigdata [12–15] and so
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