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Statistical Analysis and Prediction of Fatal Accidents in the Metallurgical Industry in China

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  • Qingwei Xu

    (College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China)

  • Kaili Xu

    (Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, School of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China)

Abstract

The metallurgical industry is a significant component of the national economy. The main purpose of this study was to establish a composite risk analysis method for fatal accidents in the metallurgical industry. We collected 152 fatal accidents in the Chinese metallurgical industry from 2001 to 2018, including 141 major accidents, 10 severe accidents, and 1 extraordinarily severe accident, together resulting in 731 deaths. Different from traffic or chemical industry accidents, most of the accidents in the metallurgical industry are poisoning and asphyxiation accidents, which account for 40% of the total number of fatal accidents. As the original statistical data of fatal accidents in the metallurgical industry have irregular fluctuations, the traditional prediction methods, such as linear or quadratic regression models, cannot be used to predict their future characteristics. To overcome this issue, the grey interval predicting method and the GM(1,1) model of grey system theory are introduced to predict the future characteristics of fatal accidents in the metallurgical industry. Different from a fault tree analysis or event tree analysis, the bow tie model integrates the basic causes, possible consequences, and corresponding safety measures of an accident in a transparent diagram. In this study, the bow tie model was used to identify the causes and consequences of fatal accidents in the metallurgical industry; then, corresponding safety measures were adopted to reduce the risk.

Suggested Citation

  • Qingwei Xu & Kaili Xu, 2020. "Statistical Analysis and Prediction of Fatal Accidents in the Metallurgical Industry in China," IJERPH, MDPI, vol. 17(11), pages 1-20, May.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:11:p:3790-:d:363446
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    References listed on IDEAS

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    1. Rahman, S. & Karanki, D.R. & Epiney, A. & Wicaksono, D. & Zerkak, O. & Dang, V.N., 2018. "Deterministic sampling for propagating epistemic and aleatory uncertainty in dynamic event tree analysis," Reliability Engineering and System Safety, Elsevier, vol. 175(C), pages 62-78.
    2. Lin, Boqiang & Xu, Mengmeng, 2018. "Regional differences on CO2 emission efficiency in metallurgical industry of China," Energy Policy, Elsevier, vol. 120(C), pages 302-311.
    3. Hua Bai & Guang Yu, 2016. "A Weibo-based approach to disaster informatics: incidents monitor in post-disaster situation via Weibo text negative sentiment analysis," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 83(2), pages 1177-1196, September.
    4. Liping Zhang & Li Wang & Yanling Zheng & Kai Wang & Xueliang Zhang & Yujian Zheng, 2017. "Time Prediction Models for Echinococcosis Based on Gray System Theory and Epidemic Dynamics," IJERPH, MDPI, vol. 14(3), pages 1-14, March.
    5. Xuejun Shen & Limin Ou & Xiaojun Chen & Xin Zhang & Xuerui Tan, 2013. "The Application of the Grey Disaster Model to Forecast Epidemic Peaks of Typhoid and Paratyphoid Fever in China," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-6, April.
    6. Cheng Miao & Mingtao Ding, 2017. "Analysis of influence of natural disaster on the economy and prediction of recovery time based on grey forecasting–difference comparison model: a case study in the upper Min River," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 85(2), pages 1135-1150, January.
    7. Qingwei Xu & Kaili Xu & Fang Zhou, 2020. "Safety Assessment of Casting Workshop by Cloud Model and Cause and Effect–LOPA to Protect Employee Health," IJERPH, MDPI, vol. 17(7), pages 1-18, April.
    8. Yan Cui & Shuang-Shuang Tian & Nan Qiao & Cong Wang & Tong Wang & Jian-Jun Huang & Chen-Ming Sun & Jie Liang & Xiao-Meng Liu, 2015. "Associations of Individual-Related and Job-Related Risk Factors with Nonfatal Occupational Injury in the Coal Workers of Shanxi Province: A Cross-Sectional Study," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-13, July.
    9. Changxi Ma & Jibiao Zhou & Dong Yang, 2020. "Causation Analysis of Hazardous Material Road Transportation Accidents Based on the Ordered Logit Regression Model," IJERPH, MDPI, vol. 17(4), pages 1-25, February.
    10. Ya-Ling Huang & Chin-Tsai Lin, 2011. "Developing an interval forecasting method to predict undulated demand," Quality & Quantity: International Journal of Methodology, Springer, vol. 45(3), pages 513-524, April.
    11. Pincheira Brown, Pablo & Hardy, Nicolás, 2019. "Forecasting base metal prices with the Chilean exchange rate," Resources Policy, Elsevier, vol. 62(C), pages 256-281.
    12. R. Rajesh & Chandrasekharan Rajendran, 2019. "Grey- and rough-set-based seasonal disaster predictions: an analysis of flood data in India," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 97(1), pages 395-435, May.
    13. Jishuo Li & Kaili Xu & Bingjie Fan & Liyan Geng, 2020. "Corrigendum to “Risk Assessment of Oxygen Lance Burning Loss Using Bow-Tie Analysis Based on Fuzzy Theory”," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-1, November.
    14. Banus Kam Leung Low & Siu Shing Man & Alan Hoi Shou Chan, 2018. "The Risk-Taking Propensity of Construction Workers—An Application of Quasi-Expert Interview," IJERPH, MDPI, vol. 15(10), pages 1-11, October.
    15. Guo, Xu & Song, Lianlian & Fang, Yun & Zhu, Lixing, 2019. "Model checking for general linear regression with nonignorable missing response," Computational Statistics & Data Analysis, Elsevier, vol. 138(C), pages 1-12.
    16. Jishuoli Li & Kaili Xu & Bingjie Fan & Liyan Geng, 2020. "Risk Assessment of Oxygen Lance Burning Loss Using Bow-Tie Analysis Based on Fuzzy Theory," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-12, February.
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    Cited by:

    1. Qingwei Xu & Kaili Xu, 2021. "Analysis of the Characteristics of Fatal Accidents in the Construction Industry in China Based on Statistical Data," IJERPH, MDPI, vol. 18(4), pages 1-21, February.
    2. Jeong-Hun Won & Hyeon-Ji Jeong & WonSeok Kim & Seungjun Kim & Sung-Yong Kang & Jong Moon Hwang, 2022. "Mechanisms Analysis for Fatal Accident Types Caused by Multiple Processes in the Workplace: Based on Accident Case in South Korea," IJERPH, MDPI, vol. 19(18), pages 1-23, September.
    3. Jing Guo & Zhen Wei & Jun Ren & Zenghai Luo & Huakun Zhou, 2020. "Early-Warning Measures for Ecological Security in the Qinghai Alpine Agricultural Area," IJERPH, MDPI, vol. 17(24), pages 1-29, December.
    4. Zifeng Liang, 2021. "Assessment of the Construction of a Climate Resilient City: An Empirical Study Based on the Difference in Differences Model," IJERPH, MDPI, vol. 18(4), pages 1-20, February.

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