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A review on supervised machine learning for accident risk analysis: challenges in Malaysia


Citation

Choo, Boon Chong and Abdul Razak, Musab and Awang Biak, Dayang Radiah and Mohd Tohir, Mohd Zahirasri and Syafiie, S. (2022) A review on supervised machine learning for accident risk analysis: challenges in Malaysia. Process Safety Progress, 41 (spec. 1). 147 - 158. ISSN 1066-8527; ESSN: 1547-5913

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.


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Additional Metadata

Item Type: Article
Divisions: Faculty of Engineering
DOI Number: https://doi.org/10.1002/prs.12346
Publisher: John Wiley & Sons
Keywords: Accident analysis; Artificial intelligence; Big data; IR 4.0; Safety decision-making
Depositing User: Ms. Nur Faseha Mohd Kadim
Date Deposited: 26 Dec 2023 04:11
Last Modified: 26 Dec 2023 04:11
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1002/prs.12346
URI: http://psasir.upm.edu.my/id/eprint/100375
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