A review on supervised machine learning for accident risk analysis: Challenges in Malaysia. (23rd February 2022)

• Record Type: Journal Article
• Authors: Choo, Boon Chong; Abdul Razak, Musab; Dayang Radiah, Awang Biak; Mohd Tohir, Mohd Zahirasri; Syafiie, S.
• Other Names: Abidin Mardhati Zainal guestEditor.; Rusli Risza Bt guestEditor.; Willey Ronald J. guestEditor.
• Is Part Of: Process safety progress. Volume 41(2022)Supplement 1
• Page Start: S147
• Page End: S158
• Publication Date: 2022-02-23
• Subjects: accident analysis -- artificial intelligence -- big data -- IR 4.0 -- safety decision‐making; Chemical plants -- Management -- Periodicals; 660
• DOI: 10.1002/prs.12346
• Languages: English
• ISSNs: 1066-8527

Abstract

Abstract: The new Fourth Industrial Revolution (IR 4.0) trend is driven by the concept of automation and artificial intelligence (AI). However, Malaysia is slightly behind Singapore in terms of adopting AI innovation among ASEAN countries. This paper aims to conduct a literature review of machine learning to overcome subjectivity and bias in risk ranking decision‐making. An introduction to machine learning concerning accident risk analysis is presented, and the challenges of its application in Malaysia are discussed. Existing machine learning features were evaluated to identify the feasible application in industrial accident analysis and ensure safety decision‐making consistency. This review observed how the IR 4.0 approaches were used in the risk analysis, especially on supervised machine learning. This study also highlights the finding from the previous works on challenges in utilizing supervised machine learning, which is the need to have publicly accessible large database from industries and agencies such as the Department of Occupational Safety and Health (DOSH) Malaysia for the development of algorithms, which can potentially improve accident risk analysis and safety, especially for Malaysian industries.