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Optimised Big Data analytics for health and safety hazards prediction in power infrastructure operations

Ajayi, Anuoluwapo; Oyedele, Lukumon; Akinade, Olugbenga; Bilal, Muhammad; Akanbi, Lukman; Delgado, Juan Manuel Davila; Owolabi, Hakeem

Authors

Olugbenga Akinade Olugbenga.Akinade@uwe.ac.uk
Associate Professor - AR/VR Development with Artificial Intelligence

Muhammad Bilal Muhammad.Bilal@uwe.ac.uk
Associate Professor - Big Data Application

Dr Lukman Akanbi Lukman.Akanbi@uwe.ac.uk
Associate Professor - Big Data Application Developer

Manuel Davila Delgado Manuel.Daviladelgado@uwe.ac.uk
Associate Professor - AR/VR Development with Artificial Intelligence

Hakeem Owolabi Hakeem.Owolabi@uwe.ac.uk
Associate Professor - Project Analytics and Digital Enterprise



Abstract

© 2020 Elsevier Ltd Forecasting imminent accidents in power infrastructure projects require a robust and accurate prediction model to trigger a proactive strategy for risk mitigation. Unfortunately, getting ready-made machine learning algorithms to eliminate redundant features optimally is challenging, especially if the parameters of these algorithms are not tuned. In this study, a particle swarm optimization is proposed both for feature selection and parameters tuning of the gradient boosting machine technique on 1,349,239 data points of an incident dataset. The predictive ability of the proposed method compared to conventional tree-based methods revealed near-perfect predictions of the proposed model on test data (classification accuracy − 0.878 and coefficient of determination − 0.93) for the two outcome variables ACCIDENT and INJURYFREQ. The high predictive power obtained reveals that injuries do not occur in a chaotic fashion, but that underlying patterns and trends exist that can be uncovered and captured via machine learning when applied to sufficiently large datasets. Also, key relationships identified will assist safety managers to understand possible risk combinations that cause accidents; helping to trigger proactive risk mitigation plans.

Journal Article Type Article
Publication Date May 1, 2020
Journal Safety Science
Print ISSN 0925-7535
Electronic ISSN 1879-1042
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 125
Article Number 104656
APA6 Citation Ajayi, A., Oyedele, L., Akinade, O., Bilal, M., Akanbi, L., Delgado, J. M. D., & Owolabi, H. (2020). Optimised Big Data analytics for health and safety hazards prediction in power infrastructure operations. Safety Science, 125, https://doi.org/10.1016/j.ssci.2020.104656
DOI https://doi.org/10.1016/j.ssci.2020.104656
Keywords Big Data analytics; Particle swarm optimization; Power infrastructure; Safety management
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