Effectiveness of Natural Language Processing Based Machine Learning in Analyzing Incident Narratives at a Mine

• Main Authors: Rajive Ganguli, Preston Miller, Rambabu Pothina
• Format: Article
• Language: English
• Published: MDPI AG 2021-07-01
• Series: Minerals
• Subjects: mine safety and health; accidents; narratives; machine learning; natural language processing; random forest classification
• Online Access: https://www.mdpi.com/2075-163X/11/7/776

To achieve the goal of preventing serious injuries and fatalities, it is important for a mine site to analyze site specific mine safety data. The advances in natural language processing (NLP) create an opportunity to develop machine learning (ML) tools to automate analysis of mine health and safety management systems (HSMS) data without requiring experts at every mine site. As a demonstration, nine random forest (RF) models were developed to classify narratives from the Mine Safety and Health Administration (MSHA) database into nine accident types. MSHA accident categories are quite descriptive and are, thus, a proxy for high level understanding of the incidents. A single model developed to classify narratives into a single category was more effective than a single model that classified narratives into different categories. The developed models were then applied to narratives taken from a mine HSMS (non-MSHA), to classify them into MSHA accident categories. About two thirds of the non-MSHA narratives were automatically classified by the RF models. The automatically classified narratives were then evaluated manually. The evaluation showed an accuracy of 96% for automated classifications. The near perfect classification of non-MSHA narratives by MSHA based machine learning models demonstrates that NLP can be a powerful tool to analyze HSMS data.