Application of natural language processing and machine learning for analyzing mining accident reports and automating the process of root cause analysis

Abstract Coal mining accidents are a major concern worldwide, necessitating effective safety measures and comprehensive analysis to prevent future accidents. Our proposed solution is the first attempt for Indian mines, inspired by the potential of Natural Language Processing (NLP) that can read and analyze vast repositories of accident records in seconds. In combination with machine learning (ML), NLP algorithms can extract unstructured text by eliminating manual data entry errors, reading poorly scanned reports, and understanding multiple versions of the event and cluster documents based on types that would otherwise take months to collate. In the case of accident records, it can be an asset in capturing recurring issues, contributing factors, and high-risk areas, enabling proactive measures to be taken to prevent future accidents. The heart of the study lies in applying two ML algorithms called latent Dirichlet allocation (LDA) and RAKE (Rapid Automatic Keyword Extraction). LDA is a topic modeling technique for clustering accidents based on descriptions. RAKE generates root cause analysis through keywords from accident descriptions and remedies suggested by inspection officers. Both are unsupervised learning techniques that do not require any training on labeled datasets. AI and NLP can significantly enhance the process of creating Swiss Cheese Models and Logic Sequences of Contributory Factors Diagrams by automating the extraction, classification, and analysis of data from incident reports and other relevant documents. Data for analysis in this study came from the Directorate General of Mines Safety (DGMS), India records from 2010 to 2015.