Learning about risk: Machine learning for risk assessment

Paltrinieri, Nicola; Comfort, Louise K.; Reniers, Genserik

doi:10.1016/j.ssci.2019.06.001

Cited by 210 publications

(80 citation statements)

References 61 publications

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“…Nevertheless, implementation has not yet been fully realised since Diekmann's [8] prophecy. On the other hand, the application of AI has become more attractive due to the progressive refinement of its models, its reduced cost, and improvement of employees' skills and lifestyle (digitalisation) as well as increases in computing power [9], [10]. This paper utilizes an ML method, the decision tree (DT) method, to show how this technique can enhance both safety and the analysis of accidents and address risk methodology gaps in railway stations.…”

Section: The Contributionmentioning

confidence: 99%

Learning From Accidents: Machine Learning for Safety at Railway Stations

Alawad

Kaewunruen

2020

IEEE Access

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Section: The Contributionmentioning

confidence: 99%

Learning From Accidents: Machine Learning for Safety at Railway Stations

Alawad

Kaewunruen

2020

IEEE Access

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“…Machine learning procedures aim to allow an agent (system, device, or program) to learn when their performance improves with experience [58]- [62]. In this work, two machine learning techniques are used to generate optimal plans for adequate attention to business risk factors: The Kmeans clustering algorithm and a decision tree classifier.…”

Section: B Machine Learningmentioning

confidence: 99%

Optimization for Risk Decision-Making Through Simulated Annealing

Eraña-Díaz¹,

Cruz-Chávez²,

Rivera-López

et al. 2020

IEEE Access

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In this paper, a computational methodology combining the simulated annealing algorithm with two machine learning techniques to select a near-optimal safeguard set for business risk response is presented. First, a mathematical model with four types of risk factor responses (avoid, mitigate, transfer, and accept) is constructed. Then, the simulated annealing algorithm is applied to find a set of near-optimal solutions to the model. Next, these solutions are processed by the k-means clustering algorithm for identifying three categories, and with a decision tree classifier, the most relevant elements of each one are obtained. Finally, the categorized solutions are shown to the decision-makers through a user interface. These stages are designed with the aim of the users can take an appropriate safeguard set and develop one specific and optimal program to respond to business risk factors. The results generated by the proposed approach are reached in a reasonable time using less computational resources than those used by other procedures. Furthermore, the best results obtained by the simulated annealing algorithm use a lower business budget, and they have a relative-error less than 0.0013% of the optimal solution given by a deterministic method.

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“…Formal models apply the probabilistic approach for assess the scenarios of risk with the use of Bayesian networks, being representative of the Why Because Analysis [86] method; fuzzy logic, Monte Carlo analysis, and Delphi procedure are additionally applied in the analysis of a container shipping logistic platform, and in a gas storage facility [146][147][148]. Industry 4.0 is a generic concept to improve self-control and risk identification through neural networks and machine learning; related to its application in industrial parks, it is used in inspection maintenance, construction, and environmental protection for chemical, oil and gas, and energy processes [149][150][151] Safety Barrier models. The representatives for this group are the Process Hazard Prevention Accident Models (PHPAM) and the System Hazard Identification Prediction and Prevention (SHIPP).…”

Section: Formal Basedmentioning

confidence: 99%

Analysis and Characterization of Risk Methodologies Applied to Industrial Parks

et al. 2020

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It is important to evaluate the risks in industrial parks and their processes due to the consequences of major accidents and especially the domino effect. Scientific works present a wide possibility of models to deal with these situations. In this work, based on the information extracted from the scientific literature, six groups of risk methodologies are defined, analyzed, and characterized with methods that cover the standards, preventive, probabilistic, traditional, modern, and dynamic evaluation that are applied or could be used in industrial parks. It also tries to achieve the objective of determining which are more appropriate if the possible situations and causes that can produce an accident are taken into account, identifying and evaluating them with characteristics of simultaneity and immediacy, determining the probability of an accident occurring with sufficient advance in time to avoid it under the use of a working operational procedure. There is no definitive methodology, and it is necessary that they complement each other, but considering the proposed objective, the integrated application of traditional methodologies together with the management of safety barriers, the dynamic evaluation of risks, and the inclusion of machine learning systems could fulfill the proposed objective.

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Learning about risk: Machine learning for risk assessment

Cited by 210 publications

References 61 publications

Learning From Accidents: Machine Learning for Safety at Railway Stations

Learning From Accidents: Machine Learning for Safety at Railway Stations

Optimization for Risk Decision-Making Through Simulated Annealing

Analysis and Characterization of Risk Methodologies Applied to Industrial Parks

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