Learning about risk: Machine learning for risk assessment. (October 2019)

• Record Type: Journal Article
• Authors: Paltrinieri, Nicola; Comfort, Louise; Reniers, Genserik
• Is Part Of: Safety science. Volume 118(2019)
• Page Start: 475
• Page End: 486
• Publication Date: 2019-10
• Subjects: Risk assessment -- Dynamic risk analysis -- Machine learning -- Deep learning; Industrial accidents -- Periodicals; Accident Prevention -- Periodicals; Safety -- Periodicals; Travail -- Accidents -- Périodiques; 363.11
• DOI: 10.1016/j.ssci.2019.06.001
• Languages: English
• ISSNs: 0925-7535

Abstract

Highlights: We suggest a risk assessment approach based on machine learning. A deep neural network (DNN) model is developed and tested. A drive-off scenario involving an Oil & Gas drilling rig was considered. Results show reasonable accuracy and suitability of DNN for risk assessment. Nevertheless, model selection and customization should be carefully carried out. Abstract: Risk assessment has a primary role in safety-critical industries. However, it faces a series of overall challenges, partially related to technology advancements and increasing needs. There is currently a call for continuous risk assessment, improvement in learning past lessons and definition of techniques to process relevant data, which are to be coupled with adequate capability to deal with unexpected events and provide the right support to enable risk management. Through this work, we suggest a risk assessment approach based on machine learning. In particular, a deep neural network (DNN) model is developed and tested for a drive-off scenario involving an Oil & Gas drilling rig. Results show reasonable accuracy for DNN predictions and general suitability to (partially) overcome risk assessment challenges. Nevertheless, intrinsic model limitations should be taken into account and appropriate model selection and customization should be carefully carried out to deliver appropriate support for safety-related decision-making.