Predicting Workplace Injuries in the Oil and Gas Industry Using LR and ABR and MLP and XGBR Regressions

It is acknowledged that the oil and gas sector is one of the most important sectors. dangerous industries worldwide, characterized by frequent occupational injuries and fatalities due to its complex processes and challenging work environments. This study addresses the urgent need for accurate injury prediction within the industry by applying advanced data analytics and machine learning techniques. Through in-depth analysis of a large dataset that includes historical injury records and operational factors, the research aims to identify trends and key indicators of injury incidents, which will pave the way for proactive safety strategies. The research methodology involves several key steps, including data cleaning, feature engineering, and implementation of predictive models such as Random Forest, XG Boost, and Decision Trees were evaluated using standard performance metrics including F1-score, recall, precision, and accuracy. Among them, XG Boost showed the best performance, providing high predictive accuracy and reliable classification results, underlining its practical applicability in industrial safety management. In addition, the study examines how various variables such as incident type, root causes, work activities, and specific work activities affect the probability of injuries. Feature importance analysis revealed that factors such as unsafe behaviors, identified root causes, and the nature of the tasks performed play a key role in injury prediction, providing valuable insights for safety planning and risk reduction. This work significantly adds to the existing literature on occupational safety in high-risk sectors and underscores the benefits of using data-driven models for workplace safety. The predictive tool developed through this research can serve as an early warning system, supporting safety personnel in recognizing hazardous situations and implementing focused preventive measures. By integrating predictive analytics into routine by following safety practices, the oil and gas industry can reduce occupational injuries., improve operational efficiency, and better protect its employees.

Publication Details

Published
2025-07-19
DOI
https://doi.org/10.46632/daai/5/1/9
Primary Topic
Occupational Health and Safety Research
Type
article
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article

Predicting Workplace Injuries in the Oil and Gas Industry Using LR and ABR and MLP and XGBR Regressions

Occupational Health and Safety Research
article

Predicting Workplace Injuries in the Oil and Gas Industry Using LR and ABR and MLP and XGBR Regressions

article en

Abstract

It is acknowledged that the oil and gas sector is one of the most important sectors. dangerous industries worldwide, characterized by frequent occupational injuries and fatalities due to its complex processes and challenging work environments. This study addresses the urgent need for accurate injury prediction within the industry by applying advanced data analytics and machine learning techniques. Through in-depth analysis of a large dataset that includes historical injury records and operational factors, the research aims to identify trends and key indicators of injury incidents, which will pave the way for proactive safety strategies. The research methodology involves several key steps, including data cleaning, feature engineering, and implementation of predictive models such as Random Forest, XG Boost, and Decision Trees were evaluated using standard performance metrics including F1-score, recall, precision, and accuracy. Among them, XG Boost showed the best performance, providing high predictive accuracy and reliable classification results, underlining its practical applicability in industrial safety management. In addition, the study examines how various variables such as incident type, root causes, work activities, and specific work activities affect the probability of injuries. Feature importance analysis revealed that factors such as unsafe behaviors, identified root causes, and the nature of the tasks performed play a key role in injury prediction, providing valuable insights for safety planning and risk reduction. This work significantly adds to the existing literature on occupational safety in high-risk sectors and underscores the benefits of using data-driven models for workplace safety. The predictive tool developed through this research can serve as an early warning system, supporting safety personnel in recognizing hazardous situations and implementing focused preventive measures. By integrating predictive analytics into routine by following safety practices, the oil and gas industry can reduce occupational injuries., improve operational efficiency, and better protect its employees.

Vol. 5(1, 2025)
Industry, innovation and infrastructure
Openalex Percentile: Top 72%
Occupational Health and Safety Research
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Predicting Workplace Injuries in the Oil and Gas Industry Using LR and ABR and MLP and XGBR Regressions · (2025) | TGRS Research Map | TGRS