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Real-Time Warning and Risk Assessment of Tailings Dam Disaster Status Based on Dynamic Hierarchy-Grey Relation Analysis

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  • Wen Li
  • Yicheng Ye
  • Nanyan Hu
  • Xianhua Wang
  • Qihu Wang

Abstract

The consequences of tailings dam breaks are disastrous; although various factors can often result in tailings dam damage, the main cause is poor management. To reduce human supervision errors and ensure that real-time early warnings alerts are sent for any risks, 22 evaluation indexes that affect dam breaks were set up based on inherent and frequency risk. To efficiently predict an early dam break signal for a tailings dam, 12 key evaluation indexes of a dynamic early warning system were screened and a comprehensive consideration of the risk trend was undertaken. The current and future states of the 12 indexes were analyzed based on a borda count and dynamic analytic hierarchy process (AHP) methods and early warning grades for tailings dam damage were evaluated using the dynamic grey relation analysis method. The dynamic AHP method, which avoids the tedious testing and risks of static early warning states, was compared to the traditional method. This research provides a useful basis upon which mining enterprises can select reasonable and effective prediction indexes for risk assessment, fully implement and promote intelligent management of major risks, and conduct accurate and authentic supervision at all levels.

Suggested Citation

  • Wen Li & Yicheng Ye & Nanyan Hu & Xianhua Wang & Qihu Wang, 2019. "Real-Time Warning and Risk Assessment of Tailings Dam Disaster Status Based on Dynamic Hierarchy-Grey Relation Analysis," Complexity, Hindawi, vol. 2019, pages 1-14, April.
    • Handle: RePEc:hin:complx:5873420
    DOI: 10.1155/2019/5873420
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    Cited by:

    1. Jia, Xiaohui & Zhang, Donghui, 2021. "Prediction of maritime logistics service risks applying soft set based association rule: An early warning model," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    2. Kai Dong & Zhankuan Mi & Dewei Yang, 2022. "Comprehensive Diagnosis Method of the Health of Tailings Dams Based on Dynamic Weight and Quantitative Index," Sustainability, MDPI, vol. 14(5), pages 1-13, March.
    3. Zhanjie Jing & Xiaohong Gao, 2022. "Monitoring and early warning of a metal mine tailings pond based on a deep learning bidirectional recurrent long and short memory network," PLOS ONE, Public Library of Science, vol. 17(10), pages 1-15, October.

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