A Comprehensive Assessment of Mining Accident Severity Using Machine Learning Methods

A Comprehensive Assessment of Mining Accident Severity Using Machine Learning Methods | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 9 August 2025 V1 Latest version Share on A Comprehensive Assessment of Mining Accident Severity Using Machine Learning Methods Authors : Irshad Ahmad Mohd Sagir Ansari 0009-0002-2686-2672 [email protected] and Ajay Kumar Gupta Authors Info & Affiliations https://doi.org/10.22541/au.175473364.44826283/v1 177 views 202 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract 30.0 The mining sector is recognized as one of the riskiest in the world. However, there is a lack of a comprehensive understanding of the factors influencing the severity of accidents. In this study, eight machine learning (ML) methods were systematically evaluated, among which Gradient Boosting and Random Forest emerged as the top-performing models. These ensemble models demonstrated consistently high performance across multiple evaluation metrics (Accuracy, Precision, Recall, F1-score, and ROC AUC), highlighting their robustness and reliability in distinguishing between fatal and non-fatal accident outcomes. Across both impurity-based (Gini importance) and robust model-agnostic (SHAP) frameworks, Risk Taking, Age, and Experience emerge as the most influential predictors. Social engagement metrics demonstrate varying levels of importance, collectively indicating that social support systems may play a meaningful role in moderating accident severity. These findings substantiate the utility of machine learning not only in accurate outcome classification but also in elucidating the multifaceted drivers underlying accident severity, thereby informing targeted intervention and prevention strategies. Supplementary Material File (wiley___machine_learning-2.pdf) Download 376.16 KB Information & Authors Information Version history V1 Version 1 09 August 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords behavioural-based schemes human-oriented security information security privacy risk assessment Authors Affiliations Irshad Ahmad Mohd Sagir Ansari 0009-0002-2686-2672 [email protected] Vivekanand Polytechnic View all articles by this author Ajay Kumar Gupta Shri Rawatpura Sarkar University View all articles by this author Metrics & Citations Metrics Article Usage 177 views 202 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Irshad Ahmad Mohd Sagir Ansari, Ajay Kumar Gupta. A Comprehensive Assessment of Mining Accident Severity Using Machine Learning Methods. Authorea . 09 August 2025. 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