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Genetic Fuzzy System for Automating Maritime Risk Assessment

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Uncertainty Management with Fuzzy and Rough Sets

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 377))

Abstract

This chapter uses genetic fuzzy systems (GFS) to assess the risk level of maritime vessels transmitting Automatic Identification System (AIS) data. Previous risk assessment approaches based on fuzzy inference systems (FIS) relied on domain experts to specify the FIS membership functions as well as the fuzzy rule base (FRB), a burdensome and time-consuming process. This chapter aims to alleviate this burden by learning the membership functions and FRB for the FIS of an existing Risk Management Framework (RMF) directly from data. The proposed methodology is tested with four different case studies in maritime risk analysis. Each case study concerns a unique scenario involving a particular region: the Gulf of Guinea, the Strait of Malacca, the Northern Atlantic during a storm, and the Northern Atlantic during a period of calm seas. The experiments compare 14 GFS algorithms from the KEEL software package and evaluate the resulting FRBs according to their accuracy and interpretability. The results indicate that IVTURS, LogitBoost, and NSLV generate the most accurate rule bases while SGERD, GCCL, NSLV, and GBML each generate interpretable rule bases. Finally, IVTURS, NSLV, and GBML algorithms offer a reasonable compromise between accuracy and interpretability.

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Notes

  1. 1.

    http://www.site.uottawa.ca/~rfalc032/isfuros2017/.

  2. 2.

    https://www.nodc.noaa.gov/woce/woce_v3/wocedata_1/woce-uot/document/wmocode.htm.

  3. 3.

    https://www.tc.gc.ca/eng/marinesafety/tp-tp14070-3587.htm.

  4. 4.

    https://www.orbcomm.com/.

  5. 5.

    ftp://polar.ncep.noaa.gov/pub/history/waves.

  6. 6.

    https://www.icc-ccs.org/.

  7. 7.

    https://github.com/SCI2SUGR/KEEL checked out on 01/05/2018.

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Acknowledgements

The authors acknowledge the financial support of the Ontario Centres of Excellence (OCE) and the National Sciences and Engineering Research Council of Canada (NSERC) for the project entitled “Big Data Analytics for the Maritime Internet of Things”.

Author information

Authors and Affiliations

  1. School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Canada

    Alexander Teske, Rafael Falcon, Rami Abielmona & Emil Petriu

  2. Research & Engineering Division, Larus Technologies Corporation, Ottawa, Canada

    Rafael Falcon & Rami Abielmona

Authors
  1. Alexander Teske
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  2. Rafael Falcon
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  3. Rami Abielmona
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  4. Emil Petriu
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Corresponding author

Correspondence to Alexander Teske .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Universidad Central "Marta Abreu" de Las Villas, Santa Clara, Villa Clara, Cuba

    Rafael Bello

  2. School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, Canada

    Rafael Falcon

  3. Department of Computer Science and Artificial Intelligence, Technical School of Informatics and Telecommunications Engineering, University of Granada, Granada, Spain

    José Luis Verdegay

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Teske, A., Falcon, R., Abielmona, R., Petriu, E. (2019). Genetic Fuzzy System for Automating Maritime Risk Assessment. In: Bello, R., Falcon, R., Verdegay, J. (eds) Uncertainty Management with Fuzzy and Rough Sets. Studies in Fuzziness and Soft Computing, vol 377. Springer, Cham. https://doi.org/10.1007/978-3-030-10463-4_19

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