Gas outburst prediction based on OD algorithm

Xiao Guang Yue*, Sanjay K. Boddhu, Ying Lu, Fuyuan Xiao, Tarita Memonen, Maia V. Cañiv

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)


In the coal production, gas outburst is a major threat of natural disasters; it caused huge casualties in China. There are also many patents about gas outburst. In order to predict gas outburst, we use a new intelligent algorithm (opposite degree algorithm). The opposite degree algorithm is based on priori value, posteriori value, priori matrix, posterior matrix and the relationship between calculation data. The experiment is conducted based on real coal mine data in China. By learning 18 groups of gas outburst data, forecasts the results of 7 groups of gas outburst data. The accuracy of experiment is 6/7. Meanwhile, we introduce the prediction experiments based on single index method and comprehensive index method and BP neural network for the result comparison. Results show that opposite degree method is as good as the BP neural network method or comprehensive index method. Gas outburst prediction based on opposite degree algorithm proved the algorithm is feasible and effective, and can be used in value prediction.

Original languageEnglish
Pages (from-to)25-39
Number of pages15
JournalRecent Patents on Computer Science
Issue number1
Publication statusPublished - 2016


  • opposite degree
  • gas outburst
  • prior value
  • posteriori value


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