Using machine learning and keyword analysis to analyze incidents and reduce risk in oil sands operations

Kurian, Daniel; Sattari, Fereshteh; Lefsrud, Lianne; Ma, Yongsheng

doi:10.1016/j.ssci.2020.104873

Cited by 25 publications

(14 citation statements)

References 19 publications

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“…Balasubramanian and Thangamani 41 ; Kurian et al 21,42 ; Xu et al 43 ; Cakir et al 44 ; Tamascelli et al 5 ). According to the findings, accident prediction models in the oil and gas industry are still in their early stages.…”

Section: Review Of Accident Data Analysis Using Machine Learning Tech...mentioning

confidence: 99%

“…According to Sarkar et al, 8,11,16 many organizations are unable to extract useful insights from incident data due to the lack of appropriate prediction models and unavailability of structured data. Some studies 20,21 have conducted automated risk assessments using ML algorithms and they are deployed in detecting abnormal events in the exploration of oil and gas.…”

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confidence: 99%

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Application of rough set theory and machine learning algorithms in predicting accident outcomes in the Indian petroleum industry

Gangadhari

Khanzode

Murthy

2022

Concurrency and Computation

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Summary Recent advancements in machine learning techniques are helping researchers to develop predictive models that assist decision‐makers to get a quick, unbiased overview of the processes. But studies using machine learning approaches in analyzing and classifying the injury narratives of the petroleum industries are still in their early stages due to data unavailability and lack of trust in these models. Comparatively, other industries such as construction, manufacturing, aviation and so forth are using the findings from the predictive models but the use of machine learning techniques in analyzing petroleum industry accident data is not gaining much importance. This study aims to use available accident data from the Indian petroleum industry to develop a classification model for predicting possible outcomes of an accident. The data used in this study comprises 194 accident reports with 20 information attributes collected during the 2016–20 period. Six different machine learning algorithms are used to analyze and classify the possible outcome of the accident. It has been found that the Xgboost algorithm has achieved 95% accuracy following multilayer perceptron with 94% accuracy. The rough set theory is also used to extract the indiscernibility relationship between the given attributes causing accident occurrence. The results indicate that “skill‐based error, supervisory violation, no personal protective equipment usage, and lack of standard operating procedure compliance” have contributed to the majority of the accidents. The findings of this study can be used to assist safety professionals in decision‐making, mitigating the root causes of contributing factors.

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Section: Review Of Accident Data Analysis Using Machine Learning Tech...mentioning

confidence: 99%

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confidence: 99%

Application of rough set theory and machine learning algorithms in predicting accident outcomes in the Indian petroleum industry

Gangadhari

Khanzode

Murthy

2022

Concurrency and Computation

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“…Arteaga et al [23] address the issue of analyzing reports on the severity of traffic crashes and extracting meaningful information for developing safety countermeasures. Kurian et al [24] apply ML and keywords analysis for defining a customized library that more efficiently supports ways to report incidents; special attention is for those with minor consequences that often lack of details useful for understanding the causes. Paltrinieri et al [25] suggest a risk assessment approach that is based on machine learning techniques, including deep neural network model.…”

Section: Introductionmentioning

confidence: 99%

Near Miss Archive: a Challenge to Share Knowledge Among Inspectors and Improve Seveso Inspections

Ansaldi¹,

Agnello²,

Pirone³

et al. 2021

Preprint

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In European Seveso Legislation for the control of the hazard of major accidents (Directive 2015/12/UE), the Safety Management System SMS is an essential obligation for managers and the authorities are required to periodically verify its adequateness through periodical inspections at Seveso sites. One of the pillars of the SMS is the collection and analysis of documents on accidents, near misses and possibly anomalies, in order to identify weaknesses and implement continuous improvement. In Italy, for a few years, the documents, gathered from all Italian Seveso sites by the inspectors, have been archived and used for research purposes. The archive currently contains some 4000 reports, collected in five years by some 100 inspectors throughout Italy. The paper discusses in the detail the challenges faced to extract the knowledge hidden in the documents and make it usable through the design of a robust model. For this aim, Machine Learning techniques have been used as a preprocessing of the reports for extracting the concepts and their relations, organized into an entity-relation model. The effectiveness of this methodology and its potentiality are pointed out by investigating a few hot topics, exploiting the information contained in the repository.

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“…At present, identifying those specific fragments which contain at least one propaganda technique and identifying the applied propaganda technique in the fragment are main tasks of the fragment-level propaganda detection. As an extension of text classification task in the field of natural language processing, there are many relevant advanced algorithms [8,10,12,19,27] which can be used for reference.…”

Section: Introductionmentioning

confidence: 99%

Span identification and technique classification of propaganda in news articles

Liu

et al. 2021

Complex Intell. Syst.

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Propaganda is a rhetorical technique designed to serve a specific topic, which is often used purposefully in news article to achieve our intended purpose because of its specific psychological effect. Therefore, it is significant to be clear where and what propaganda techniques are used in the news for people to understand its theme efficiently during our daily lives. Recently, some relevant researches are proposed for propaganda detection but unsatisfactorily. As a result, detection of propaganda techniques in news articles is badly in need of research. In this paper, we are going to introduce our systems for detection of propaganda techniques in news articles, which is split into two tasks, Span Identification and Technique Classification. For these two tasks, we design a system based on the popular pretrained BERT model, respectively. Furthermore, we adopt the over-sampling and EDA strategies, propose a sentence-level feature concatenating method in our systems. Experiments on the dataset of about 550 news articles offered by SEMEVAL show that our systems perform state-of-the-art.

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Using machine learning and keyword analysis to analyze incidents and reduce risk in oil sands operations

Cited by 25 publications

References 19 publications

Application of rough set theory and machine learning algorithms in predicting accident outcomes in the Indian petroleum industry

Application of rough set theory and machine learning algorithms in predicting accident outcomes in the Indian petroleum industry

Near Miss Archive: a Challenge to Share Knowledge Among Inspectors and Improve Seveso Inspections

Span identification and technique classification of propaganda in news articles

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