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State Aviation Risk Assessment Level Determination Using Hierarchical Fuzzy Inference System Based on Cognitive Maps

Alvimar de Lucena Costa Junior 1,*, Mischel Carmen Neyra Belderrain 2, Moacyr Machado Cardoso Junior 3

Corresponding Author:

Alvimar de Lucena Costa Junior

Affiliation(s):

1 Gestão e Apoio à Decisão / Ciências Fundamentais / Instituto Tecnológico de Aeronáutica
Praça Marechal Eduardo Gomes, 50 Vila das Acácias, 12228-900 São José dos Campos/SP – Brasil
2 Gestão e Apoio à Decisão / Ciências Fundamentais / Instituto Tecnológico de Aeronáutica
Praça Marechal Eduardo Gomes, 50 Vila das Acácias, 12228-900 São José dos Campos/SP – Brasil
3 CNPJ: 64.037.492/0001-72 / ITA/FCMF / Instituto Tecnológico de Aeronáutica / Fundação Casimiro Montenegro Filho
Praça Marechal Eduardo Gomes, 50 Vila das Acácias, 12228-900 São José dos Campos/SP – Brasil
*Corresponding Author

Abstract:

During 2018, ICAO (International Civil Aviation Organization, a specialized UN organization) made available the results of its USOAP (Universal Safety Oversight Audit Program): the ratio of compliance for each ICAO member State to 1047 aviation safety-related protocol questions, divided into eight audit areas. Numbers itself has little meaning, even for aviation personnel. Using Cognitive Mapping (CogMap), a Problem Structuring Method tool, this paper develops a framework to extract and organize information from aviation specialists, allowing define Risk Assessment Level for each State, and for each Aviation Safety Branch defined. Using Fuzzy Inference Systems (FIS), helpful supporting decision making, Big Data available from ICAO is converted to Risk Levels for each State and audit area, what may be used to make informed better Safety decisions on the World Aviation Market. Up to the moment, there’s no evidence on the literature of using CogMap to establish a FIS.

Keywords:

Hierarchical Fuzzy Inference System, Cognitive Mapping, Risk Assessment, Problem Structuring Methods, Civil Aviation

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Cite This Paper:

Alvimar de Lucena Costa Junior, Mischel Carmen Neyra Belderrain, Moacyr Machado Cardoso Junior (2021). State Aviation Risk Assessment Level Determination Using Hierarchical Fuzzy Inference System Based on Cognitive Maps. Journal of Artificial Intelligence and Systems, 3, 1–15. https://doi.org/10.33969/AIS.2021.31001.

References:

[1] Ackermann F., Eden C., Cropper S. (1992) Getting Started with Cognitive Mapping. 7th Young OR Conference.
[2] Axelrod R. (1976). Structure of Decision: the Cognitive Maps of Political Elites. Princeton, NJ: Princeton University Press. ISBN 9780691644165
[3] ICAO, International Civil Aviation Organization. (2014) Doc 9735 AN/960 Universal Safety Oversight Audit Programme Continuous Monitoring Manual. Fourth Edition. ISBN 978-92-9249-633-3.
[4] ICAO, International Civil Aviation Organization. (2019). Web page. https://www.icao.int/safety/cmaforum/Pages/default.aspx. Access on 2019-04-27
[5] Ishizaka A. (2013). Multi-Criteria Decision Analysis: Methods and Software. John Wiley&Sons. Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, United Kingdom. ISBN 978-1-119-97407-9
[6] Karli G. (2012). Class Notes Web Page. https://www.ibu.edu.ba/assets/userfiles/it/2012/eee-Fuzzy-1.pdf. International Burch University. Access on 2019-04-25
[7] Kelly GA. (1955) The Psychology of Personal Constructs. 2nd edn. Routledge: London. ISBN 0-203-71421-0
[8] Kosko B. (1986) Fuzzy Cognitive Maps. International Journal of Man-Machine Studies. Vol 24, issue 1, 65-75. http://dx.doi.org/10.1016/S0020-7373(86)80040-2
[9] Mamdani E. H. and Assilian S. (1975) An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller. International Journal of Man-Machine Studies. Vol 7, 1-13. http://dx.doi.org/10.1016/S0020-7373(75)80002-2
[10] Raju G. V. S., Zhou J. and Kisner R. A. (1991) Hierarchical fuzzy Control. International Journal of Control, Vol.54, No.12 (5), 1201-1216. http://dx.doi.org/10.1080/00207179108934205
[11] Rosenhead J. and Mingers J. (2001) Rational Analysis for a Problematic World: Problem Structuring Methods for Complexity, Uncertainty and Conflict, 2nd edn, Chichester. John Wiley and Sons. 366p ISBN 978-0-471-49523-9
[12] Sodhi B and Prabhakar T.V. (2017) A Simplified Description of Fuzzy TOPSIS. arXiv:1205.5098v2.
[13] Vidal R.V.V. (2006) Operational Research: a Multidisciplinary Field. Pesquisa Operacional, v.26, n.1, 69-90. http://dx.doi.org/10.1590/S0101-74382006000100004
[14] Wang L.X. (1999) Analysis and Design of Hierarchical Fuzzy Systems. IEEE Transactions on Fuzzy Systems, Vol. 7, no. 5. 617-624. http://dx.doi.org/10.1109/91.797984
[15] Zadeh L. A. (1965) Fuzzy Sets. Information and Control 8, 338-353. http://dx.doi.org/10.1016/S0019-9958(65)90241-X
[16] Efe B. and Kurt M. (2019) A novel approach recommendation for hazard analysis. International Journal of Occupational Safety and Ergonomics, v. 0, n. 0, p. 1–19. https://doi.org/10.1080/10803548.2019.1648738