A polynomial neural network approach for improving risk assessment and industrial safety

Alexander Guzman-Urbina, Atsushi Aoyama, Eugene Choi

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


The estimation of the magnitude of accidental risks is one of the key elements for decision making in safety of industrial infrastructure. However, modeling industrial accidents involves very complex structures of calculation due to the considerable number of variables and large degree of uncertainty. Therefore, this paper aims to introduce a polynomial neural network approach to estimating accidental risks by using the grouping method of data handling (GMDH), which is able to deal efficiently with the complexity of the data by optimizing the configuration of the risk assessment model. The findings of this study show that accidental risk values could be improved using a GMDH-type neural network 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 GMDH-type neural network for risk assessment.

Original languageEnglish
Pages (from-to)97-107
Number of pages11
JournalICIC Express Letters
Issue number2
Publication statusPublished - 2018 Feb
Externally publishedYes


  • Artificial intelligence
  • GMDH
  • Industrial safety
  • Polynomial neural network
  • Risk assessment

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science(all)


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