Research on a Risk Early Warning Mathematical Model Based on Data Mining in China’s Coal Mine Management

Kai Yu, Lujie Zhou, Pingping Liu, Jing Chen, Dejun Miao () and Jiansheng Wang
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Kai Yu: College of Safety and Environmental Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Lujie Zhou: College of Safety and Environmental Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Pingping Liu: College of Safety and Environmental Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Jing Chen: College of Safety and Environmental Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Dejun Miao: College of Safety and Environmental Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Jiansheng Wang: Huaneng Lingtai Shaozhai Coal Industry Co., Ltd., Pingliang 744400, China

Mathematics, 2022, vol. 10, issue 21, 1-20

Abstract: The degree of informatization of coal mine safety management is becoming higher and higher, and a large amount of information is generated in this process. How to convert the existing information into useful data for risk control has become a challenge. To solve this challenge, this paper studies the mathematical model of coal mine risk early warning in China based on data mining. Firstly, the coal mine risk data was comprehensively analyzed to provide basic data for the risk prediction model of data mining. Then, the adaptive neuro-fuzzy inference system (ANFIS) was optimized twice to build the coal mine risk prediction model. By optimizing the calculation method of the control chart, the coal mine risk early warning system was proposed. Finally, based on the coal mine risk early warning model, the software platform was developed and applied to coal mines in China to control the risks at all levels. The results show that the error of the optimized ANFIS was reduced by 66%, and the early warning error was reduced by 57%. This study aimed to provide implementation methods and tools for coal mine risk management and control, and data collected has reference significance for other enterprises.

Keywords: risk; early warning; mathematical model; data mining; coal mine management (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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