Chemical Safety Risk Identification and Analysis Based on Improved LDA Topic Model and Bayesian Networks

The traditional chemical safety management method mainly relies on manual inspection and empirical judgment, which is incompetent in the face of the increasingly complex production environment and colossal data volume, and there is an urgent need to apply efficient modern emerging technologies to strengthen the safety management of chemical production sites. Therefore, this dissertation researches chemical safety risk factor identification and analysis predicated on improved LDA topic model and Bayesian network. Thirty-three main risk factors are obtained by constructing the LDA topic model, text mining, and thematic analysis of chemical safety accident cases and combining them with the socio-technical system accident model. The correlation and causal relationship between risk factors were revealed based on association rule mining and Bayesian network analysis. Sensitivity and critical causal path analyses were utilized to indicate the possible paths and vital aspects of accident development. The results show that the text mining LDA topic model proposed in the dissertation performs well in analyzing accident reports and can effectively solve the problems of insufficient analyzing ability and high subjectivity of traditional methods. The research method of the thesis can efficiently extract the keywords of accident reports and reveal the correlation and causality between risk factors.