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Data Mining Implementations for Determining Root Causes and Precautions of Occupational Accidents in Underground Hard
Bilal Altındiş1, Fatih Bayram2
1TTK Turkish Hard Coal Corporation, Türkiye.
Safety and Health at Work
|December 19, 2024
Summary
Data mining identified key factors contributing to mining accidents, including worker experience and shift hours. This analysis provides practical insights for improving occupational safety and preventing future injuries in underground coal mines.
Area of Science:
- Mining Engineering
- Data Science
- Occupational Safety and Health
Background:
- Mining operations generate vast amounts of productivity and safety data.
- Transforming this data into actionable information is crucial for enterprise success.
- Data mining offers powerful tools for extracting valuable insights from complex datasets.
Purpose of the Study:
- To analyze occupational accident data from an underground hard coal mine (2010-2021).
- To identify root causes of injuries using data mining techniques.
- To provide practical recommendations for accident prevention.
Main Methods:
- Utilized injured accident data spanning multiple years.
- Employed data mining algorithms, including k-Nearest Neighbors and Support Vector Machines, via the WEKA program.
- Applied association rules mining (Apriori, Predictive Apriori) to determine accident causality.
Main Results:
- k-Nearest Neighbors and Support Vector Machines demonstrated strong predictive performance.
- Association rules mining identified worker experience, shift hours, and position as primary accident drivers.
- Monthly production, wages, and accident causes also showed significant influence.
Conclusions:
- The findings align with the operational realities of the mining enterprise.
- The study offers practical suggestions for evaluating and mitigating occupational accidents.
- Data-driven insights can enhance safety protocols and reduce injury rates.
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