Machine Learning-based Hazard Classification

Machine Learning-Based Hazard Classification is a methodological framework within hazard classification that employs machine learning algorithms to automatically identify, categorize, and prioritize hazards by analyzing patterns in data, distinguishing itself from traditional rule-based or expert-driven approaches through its reliance on data-driven model training and predictive analytics.

72

Publications

3.8K

Citations

264

Authors

141

Institutions

Publications per year

2017–2026

66

Authors

264

  1. XX
    Xuecai Xie

    China University of Mining and Technology, China

    2 publications · 187 citations

    2

    h-index

  2. GF
    Gui Fu

    China University of Mining and Technology, China

    2 publications · 187 citations

    2

    h-index

  3. BP
    Biswajeet Pradhan

    University of Technology Sydney, Australia

    2 publications · 253 citations

    2

    h-index

  4. LL
    Lianne Lefsrud

    University of Alberta, Canada

    2 publications · 38 citations

    2

    h-index

  5. FS
    Fereshteh Sattari

    University of Alberta, Canada

    2 publications · 38 citations

    2

    h-index

1–5 of 264

Venues

  1. Process Safety and Environmental Protection

    6 publications · 514 citations

    6

    h-index

  2. Reliability Engineering & System Safety

    4 publications · 244 citations

    4

    h-index

  3. Safety Science

    3 publications · 83 citations

    3

    h-index

  4. The Science of The Total Environment

    2 publications · 253 citations

    2

    h-index

  5. Remote Sensing

    2 publications · 138 citations

    2

    h-index

1–5