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Evaluation of Chinese Enterprise Safety Production Resilience Based on a Combined Gray Relevancy and BP Neural Network Model

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  • Jingjing Pei

    (School of Engineering & Technology, China University of Geosciences-Beijing, Beijing 100083, China)

  • Wen Liu

    (School of Engineering & Technology, China University of Geosciences-Beijing, Beijing 100083, China)

Abstract

Improving the resilience of enterprise safety production is one of the important ways to deal with the frequency of safety accidents. Based on the definition of enterprise safety production resilience, we fully consider the impacts of recovery resilience, self-organizing resilience, and learning resilience as the three dimensions of enterprise safety production resilience. We build a back propagation (BP) neural network model that analyzes the main factors of enterprise safety production resilience using the results of gray relational analysis as an input that can assess the resilience of enterprise safety production and provide a valuable reference for the improvement of an enterprise’s safety production level. The results show that the resilience of production safety obviously increased after the Chinese enterprises with low resilience (as predicted by the model) adopted the corresponding early warning methods. The gray relational degree analysis method can incorporate well the variables for the establishment of the BP neural network prediction model.

Suggested Citation

  • Jingjing Pei & Wen Liu, 2019. "Evaluation of Chinese Enterprise Safety Production Resilience Based on a Combined Gray Relevancy and BP Neural Network Model," Sustainability, MDPI, vol. 11(16), pages 1-13, August.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:16:p:4321-:d:256369
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    References listed on IDEAS

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    1. Bergström, Johan & van Winsen, Roel & Henriqson, Eder, 2015. "On the rationale of resilience in the domain of safety: A literature review," Reliability Engineering and System Safety, Elsevier, vol. 141(C), pages 131-141.
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    Cited by:

    1. Lu Liu & Jingjing Pei & Huiquan Wang & Yun Luo, 2023. "The Evaluation and Obstacle Analysis of Urban Safety Resilience Based on Multi-Factor Perspective in Beijing," Land, MDPI, vol. 12(10), pages 1-29, October.
    2. Beatriz Andres & Giulio Marcucci, 2020. "A Strategies Alignment Approach to Manage Disruptive Events in Collaborative Networks," Sustainability, MDPI, vol. 12(7), pages 1-23, March.
    3. Fangzhong Qi & Leilei Zhang & Kexiang Zhuo & Xiuyan Ma, 2022. "Early Warning for Manufacturing Supply Chain Resilience Based on Improved Grey Prediction Model," Sustainability, MDPI, vol. 14(20), pages 1-16, October.
    4. Huafang Huang & Xiaomao Wu & Xianfu Cheng, 2020. "The Analysis of the Urban Sprawl Measurement System of the Yangtze River Economic Belt, Based on Deep Learning and Neural Network Algorithm," IJERPH, MDPI, vol. 17(12), pages 1-13, June.

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