TY - GEN
T1 - GmDH deep learning approach improving safety and risk assessment in pipelines
AU - Alexander, Guzman Urbina
AU - Atsushi, Aoyama
AU - Eugene, Choi
N1 - Funding Information:
The corresponding author would like to extend his gratitude to the Otsuka Toshimi Scholarship Foundation for contribute to the realization this research paper.
Publisher Copyright:
© 2020 26th International Association for Management of Technology Conference, IAMOT 2017. All rights reserved.
PY - 2020
Y1 - 2020
N2 - The sustainability of traditional technologies employed in energy and chemical infrastructure brings a big challenge for our society. Making decisions related with safety of industrial infrastructure, the values of accidental risk are becoming relevant points for discussion. However the challenge is the reliability of the models employed to get the risk data. Such models usually involve large number of variables, high complexity and large amounts of uncertainty. The most efficient techniques to overcome those problems are built using Artificial Intelligence (AI), and more specifically using hybrid systems and deep learning. Therefore, this paper aims to introduce a well-trained algorithm for risk assessment using the Grouping Method of Data Handling, which could be capable to deal efficiently with the complexity and uncertainty. The method of Deep Learning for risk assessment involves a regression analysis called group method of data handling (GMDH), which consists in the determination of the optimal configuration of the risk assessment model and its parameters employing polynomial theory. The Findings of this study shows that risk values could be improved using deep learning algorithms in contrast with the traditional methods by increasing the precision of risk estimation. Additional to this contribution, this study highlights the sensible and critical parameters of the learning system.
AB - The sustainability of traditional technologies employed in energy and chemical infrastructure brings a big challenge for our society. Making decisions related with safety of industrial infrastructure, the values of accidental risk are becoming relevant points for discussion. However the challenge is the reliability of the models employed to get the risk data. Such models usually involve large number of variables, high complexity and large amounts of uncertainty. The most efficient techniques to overcome those problems are built using Artificial Intelligence (AI), and more specifically using hybrid systems and deep learning. Therefore, this paper aims to introduce a well-trained algorithm for risk assessment using the Grouping Method of Data Handling, which could be capable to deal efficiently with the complexity and uncertainty. The method of Deep Learning for risk assessment involves a regression analysis called group method of data handling (GMDH), which consists in the determination of the optimal configuration of the risk assessment model and its parameters employing polynomial theory. The Findings of this study shows that risk values could be improved using deep learning algorithms in contrast with the traditional methods by increasing the precision of risk estimation. Additional to this contribution, this study highlights the sensible and critical parameters of the learning system.
KW - Artificial Intelligence
KW - GMDH learning
KW - Pipelines
KW - Risk Assessment
UR - http://www.scopus.com/inward/record.url?scp=85080856914&partnerID=8YFLogxK
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M3 - Conference contribution
AN - SCOPUS:85080856914
T3 - 26th International Association for Management of Technology Conference, IAMOT 2017
SP - 1490
EP - 1497
BT - 26th International Association for Management of Technology Conference, IAMOT 2017
PB - International Association for Management of Technology Conference (IAMOT) and the Graduate School of Technology Management, University of Pretoria
T2 - 26th International Association for Management of Technology Conference, IAMOT 2017
Y2 - 14 May 2017 through 18 May 2017
ER -