Coal and gas outburst risk prediction based on the F-SPA model

ABSTRACT

As the depth of coal mining is gradually increased in China, the intensity of related coal and gas outbursts will become ever-greater. Accurate forecasting of coal and gas outburst is the key to preventing and controlling such incidents. This study uses gray relational analysis (GRA) to identify the four main geological and environmental indicators of coal and gas outburst risk based on the characteristics of the factors influencing coal and gas outburst and their relationships with outburst strength. A Fuzzy SPA (F-SPA) model was constructed using set pair analysis and the coupled weighting method, and coal and gas outburst intensity level was predicted for 25 locations in coal mine No. 8 in the Pingdingshan mining area using the F-SPA model and applying a confidence criterion. The forecast results were compared with those derived using three other methods as well as against actual outbursts that have occurred at those locations. The results of this comparison show that the method proposed in this study has a forecast accuracy of up to 96%. The reliability of the prediction model is verified by error analysis. This case study demonstrates the reliability of the proposed method and provides a new tool for the prevention and control of coal and gas outburst hazards.

KEYWORDS:

• Coal and gas outburst
• gray relational analysis (GRA)
• set pair analysis (SPA)
• confidence judgment
• prediction model
• prediction accuracy

Acknowledgments

The authors are grateful to the editor and anonymous reviewers for their suggestions in improving the quality of the paper.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [51274206,51404277].