Identification of hidden disaster causing factors in coal mine based on Naive Bayes algorithm

Article type: Research Article

Authors: Zhao, Yifan^{a; b} | Tian, Shuicheng^{a; b; *}

Affiliations: [a] College of Safety Science and Engineering, Xi’an University of Science and Technology, Xi’an, China | [b] Institute of Safety and Emergency Management, Xi’an University of Science and Technology, Xi’an, China

Correspondence: [*] Corresponding author. Shuicheng Tian. E-mail: [email protected].

Abstract: In order to overcome the problems of low recognition rate and long recognition time existing in traditional methods, a method for identifying hidden disaster factors in coal mines based on Naive Bayes algorithm was proposed. The posterior probability of Bayesian network is calculated to obtain the maximum value of the posterior probability, so as to judge the categories of hidden disaster factors in coal mines. The method of combining soft and hard threshold functions is used to denoise Naive Bayes network. Combined with the structural equation of coal mine concealed disaster-causing factors, the index weight of coal mine disaster-causing factors is calculated, and a fast identification model of disaster-causing factors is built to complete the identification. Experimental results show that the quality factors of the proposed method are all higher than 8, the recognition rate is as high as 98%, and the recognition time is basically controlled within 0.8 s.

Keywords: Naive bayes algorithm, coal mine, hidden disaster factors, identification

DOI: 10.3233/JIFS-202726

Journal: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 2823-2831, 2021