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  4. Vol 9, Issue 1, 2024

Statistical analysis of coal mine accidents from 2012 to 2023 and trend prediction in the future in China

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DOI: 10.23977/jeis.2024.090115 | Downloads: 3 | Views: 86

Author(s)

Kehan Liu 1

Affiliation(s)

1 College of Safety Science and Engineering, Henan Polytechnic University, Jiaozuo, China

Corresponding Author

Kehan Liu

ABSTRACT

Statistical analysis of coal mine accidents plays an important role in reducing the probability of accidents and promoting coal mine safety management. However, the current research on the year, region, accident type and so on has some emphasis and many cross. In order to comprehensively and systematically master the characteristics and laws of coal mine accidents in China in recent years, this paper analyzes and discusses the number of coal mine accidents and the number of deaths in China based on the statistical data of coal mine accidents from 2012 to 2023, from the six aspects of year, month, period, region, accident grade and accident type. Based on this, the grey Markov model is used to predict and analyze the future trend of coal mine accidents in China. The research shows that the number of coal mine accidents and the number of deaths in China have shown a downward trend in recent years, but with tortuosity and recurrence, and more attention should be paid to general accidents. The high-incidence period of accidents is March, August, November and 10:00-13:59 every day. The incidence and mortality of gas and roof accidents are higher. In the large and above accidents, flood, fire, poisoning, and suffocation accidents account for a large proportion. It is predicted that the number of accidents in China in 2024 and 2025 will be 95 and 64, and the number of deaths will be 133 and 99.

KEYWORDS

Coal mine accident, Statistical analysis, Trend forecast, Grey GM (1, 1) model, The gray Markov prediction model

CITE THIS PAPER

Kehan Liu, Statistical analysis of coal mine accidents from 2012 to 2023 and trend prediction in the future in China. Journal of Electronics and Information Science (2024) Vol. 9: 103-116. DOI: http://dx.doi.org/10.23977/10.23977/jeis.2024.090115.

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