IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v17y2020i11p3790-d363446.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/17/11/3790/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/17/11/3790/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Pincheira Brown, Pablo & Hardy, Nicolás, 2019. "Forecasting base metal prices with the Chilean exchange rate," Resources Policy, Elsevier, vol. 62(C), pages 256-281.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    3. 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.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Altay, Nezih & Narayanan, Arunachalam, 2022. "Forecasting in humanitarian operations: Literature review and research needs," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1234-1244.
    2. Sankaralingam, Ravikumar & Sengottuvelan, Balasubramanian & Venkat, Pranesh & Selvaraj, Mahalingam & Arunachalam, Velmurugan & Natarajan, Jeyakumaran, 2020. "Experimental investigation on varying flame characteristics of benzoic resin solid fuel pellets," Renewable Energy, Elsevier, vol. 147(P1), pages 1500-1510.
    3. Henriques, Irene & Sadorsky, Perry, 2023. "Forecasting rare earth stock prices with machine learning," Resources Policy, Elsevier, vol. 86(PA).
    4. A. Azadeh & M. Saberi & A. Gitiforouz, 2013. "An integrated fuzzy mathematical model and principal component analysis algorithm for forecasting uncertain trends of electricity consumption," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(4), pages 2163-2176, June.
    5. Ozdemir, Ali Can & Buluş, Kurtuluş & Zor, Kasım, 2022. "Medium- to long-term nickel price forecasting using LSTM and GRU networks," Resources Policy, Elsevier, vol. 78(C).
    6. Reus, Lorenzo & Carrasco, José A. & Pincheira, Pablo, 2020. "Do it with a smile: Forecasting volatility with currency options," Finance Research Letters, Elsevier, vol. 34(C).
    7. Pincheira, Pablo & Hardy, Nicolás, 2021. "Forecasting aluminum prices with commodity currencies," Resources Policy, Elsevier, vol. 73(C).
    8. Ruyun Cheng & Li Yao & Xinyang Yan & Bingda Zhang & Zhao Jin, 2021. "High Flexibility Hybrid Architecture Real-Time Simulation Platform Based on Field-Programmable Gate Array (FPGA)," Energies, MDPI, vol. 14(19), pages 1-16, September.
    9. Zhijie Sasha Dong & Lingyu Meng & Lauren Christenson & Lawrence Fulton, 2021. "Social media information sharing for natural disaster response," 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. 107(3), pages 2077-2104, July.
    10. Siu Shing Man & Ruifeng Yu & Tingru Zhang & Alan Hoi Shou Chan, 2022. "How Optimism Bias and Safety Climate Influence the Risk-Taking Behavior of Construction Workers," IJERPH, MDPI, vol. 19(3), pages 1-14, January.
    11. Guo, Honggang & Wang, Jianzhou & Li, Zhiwu & Lu, Haiyan & Zhang, Linyue, 2022. "A non-ferrous metal price ensemble prediction system based on innovative combined kernel extreme learning machine and chaos theory," Resources Policy, Elsevier, vol. 79(C).
    12. Jin Tian & Yundou Wang & Shutian Gao, 2022. "Analysis of Mining-Related Injuries in Chinese Coal Mines and Related Risk Factors: A Statistical Research Study Based on a Meta-Analysis," IJERPH, MDPI, vol. 19(23), pages 1-16, December.
    13. Borkowski, Bolesław & Krawiec, Monika & Karwański, Marek & Szczesny, Wiesław & Shachmurove, Yochanan, 2021. "Modeling garch processes in base metals returns using panel data," Resources Policy, Elsevier, vol. 74(C).
    14. Xu, Gaowei & Azhari, Fae, 2022. "Data-driven optimization of repair schemes and inspection intervals for highway bridges," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    15. Fernandez, Viviana & Pastén-Henríquez, Boris & Tapia-Griñen, Pablo & Wagner, Rodrigo, 2023. "Commodity prices under the threat of operational disruptions: Labor strikes at copper mines," Journal of Commodity Markets, Elsevier, vol. 32(C).
    16. Ming Sun & Ronggui Zhou, 2023. "Investigation on Hazardous Material Truck Involved Fatal Crashes Using Cluster Correspondence Analysis," Sustainability, MDPI, vol. 15(12), pages 1-21, June.
    17. Pincheira-Brown, Pablo & Neumann, Federico, 2020. "Can we beat the Random Walk? The case of survey-based exchange rate forecasts in Chile," Finance Research Letters, Elsevier, vol. 37(C).
    18. Ernest Owusu Boakye & Kari Heimonen & Juha Junttila, 2024. "Commodity markets and the global macroeconomy: evidence from machine learning and GVAR," Empirical Economics, Springer, vol. 67(5), pages 1919-1965, November.
    19. Mohamed Ali Kammoun & Sadok Turki & Nidhal Rezg, 2020. "Optimization of Flight Rescheduling Problem under Carbon Tax," Sustainability, MDPI, vol. 12(14), pages 1-19, July.
    20. Kwas, Marek & Paccagnini, Alessia & Rubaszek, Michał, 2021. "Common factors and the dynamics of industrial metal prices. A forecasting perspective," Resources Policy, Elsevier, vol. 74(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:17:y:2020:i:11:p:3790-:d:363446. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.