A data-driven Bayesian Network for risk modeling and causal analysis of global maritime accidents

H. Y. Jiang; J. F. Zhang; C. P. Wan; M. Y. Zhang; C. Guedes Soares

doi:10.1201/9781003508762-36

A data-driven Bayesian Network for risk modeling and causal analysis of global maritime accidents

H. Y. Jiang, J. F. Zhang, C. P. Wan, M. Y. Zhang, C. Guedes Soares

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

Abstrakti

Multiple factors may cause maritime accidents. Investigating the impact of risk factors on maritime accidents is imperative. This paper employs a data-driven Bayesian network approach to explore the impact of risk factors on maritime safety using a large dataset of maritime accidents. The interdependencies among risk influencing factors are modeled using a Tree Augmented Network, followed by the sensitivity analysis and model validation. The results indicate that the key risk influencing factors influencing maritime accidents mainly include ship location, type, age, gross tonnage, and deadweight tonnage. This study contributes to the prevention of specific types of maritime accidents.

Alkuperäiskieli	Englanti
Otsikko	Advances in Maritime Technology and Engineering
Toimittajat	C. Guedes Soares, Tiago A. Santos
Kustantaja	CRC Press
Sivut	287-295
Sivumäärä	9
Vuosikerta	1
ISBN (elektroninen)	978-1-003-50876-2
ISBN (painettu)	978-1-032-83099-5, 978-1-032-83310-1
DOI - pysyväislinkit	https://doi.org/10.1201/9781003508762-36
Tila	Julkaistu - 2024
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisussa
Tapahtuma	International Conference on Maritime Technology and Engineering - Lisbon, Portugali Kesto: 14 toukok. 2023 → 16 toukok. 2023 Konferenssinumero: 7

Julkaisusarja

Nimi	Proceedings in Marine Technology and Ocean Engineering
Vuosikerta	13
ISSN (painettu)	2638-647X
ISSN (elektroninen)	2638-6461

Conference

Conference	International Conference on Maritime Technology and Engineering
Lyhennettä	MARTECH
Maa/Alue	Portugali
Kaupunki	Lisbon
Ajanjakso	14/05/2023 → 16/05/2023

Pääsy asiakirjaan

10.1201/9781003508762-36

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Jiang, H. Y., Zhang, J. F., Wan, C. P., Zhang, M. Y., & Soares, C. G. (2024). A data-driven Bayesian Network for risk modeling and causal analysis of global maritime accidents. teoksessa C. G. Soares, & T. A. Santos (Toimittajat), Advances in Maritime Technology and Engineering (Vuosikerta 1, Sivut 287-295). (Proceedings in Marine Technology and Ocean Engineering; Vuosikerta 13). CRC Press. https://doi.org/10.1201/9781003508762-36

@inproceedings{4d31e955e8a04da09b681d39ea34487e,

title = "A data-driven Bayesian Network for risk modeling and causal analysis of global maritime accidents",

abstract = "Multiple factors may cause maritime accidents. Investigating the impact of risk factors on maritime accidents is imperative. This paper employs a data-driven Bayesian network approach to explore the impact of risk factors on maritime safety using a large dataset of maritime accidents. The interdependencies among risk influencing factors are modeled using a Tree Augmented Network, followed by the sensitivity analysis and model validation. The results indicate that the key risk influencing factors influencing maritime accidents mainly include ship location, type, age, gross tonnage, and deadweight tonnage. This study contributes to the prevention of specific types of maritime accidents.",

author = "Jiang, {H. Y.} and Zhang, {J. F.} and Wan, {C. P.} and Zhang, {M. Y.} and Soares, {C. Guedes}",

note = "Publisher Copyright: {\textcopyright} 2024 The Author(s).; International Conference on Maritime Technology and Engineering, MARTECH ; Conference date: 14-05-2023 Through 16-05-2023",

year = "2024",

doi = "10.1201/9781003508762-36",

language = "English",

isbn = "978-1-032-83099-5",

volume = "1",

series = "Proceedings in Marine Technology and Ocean Engineering",

publisher = "CRC Press",

pages = "287--295",

editor = "Soares, {C. Guedes} and Santos, {Tiago A.}",

booktitle = "Advances in Maritime Technology and Engineering",

address = "United States",

}

Jiang, HY, Zhang, JF, Wan, CP, Zhang, MY & Soares, CG 2024, A data-driven Bayesian Network for risk modeling and causal analysis of global maritime accidents. julkaisussa CG Soares & TA Santos (toim), Advances in Maritime Technology and Engineering. Vuosikerta 1, Proceedings in Marine Technology and Ocean Engineering, Vuosikerta 13, CRC Press, Sivut 287-295, International Conference on Maritime Technology and Engineering, Lisbon, Portugali, 14/05/2023. https://doi.org/10.1201/9781003508762-36

A data-driven Bayesian Network for risk modeling and causal analysis of global maritime accidents. / Jiang, H. Y.; Zhang, J. F.; Wan, C. P. et al.
Advances in Maritime Technology and Engineering. toim. / C. Guedes Soares; Tiago A. Santos. Vuosikerta 1 CRC Press, 2024. s. 287-295 (Proceedings in Marine Technology and Ocean Engineering; Vuosikerta 13).

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

TY - GEN

T1 - A data-driven Bayesian Network for risk modeling and causal analysis of global maritime accidents

AU - Jiang, H. Y.

AU - Zhang, J. F.

AU - Wan, C. P.

AU - Zhang, M. Y.

AU - Soares, C. Guedes

N1 - Conference code: 7

PY - 2024

Y1 - 2024

N2 - Multiple factors may cause maritime accidents. Investigating the impact of risk factors on maritime accidents is imperative. This paper employs a data-driven Bayesian network approach to explore the impact of risk factors on maritime safety using a large dataset of maritime accidents. The interdependencies among risk influencing factors are modeled using a Tree Augmented Network, followed by the sensitivity analysis and model validation. The results indicate that the key risk influencing factors influencing maritime accidents mainly include ship location, type, age, gross tonnage, and deadweight tonnage. This study contributes to the prevention of specific types of maritime accidents.

AB - Multiple factors may cause maritime accidents. Investigating the impact of risk factors on maritime accidents is imperative. This paper employs a data-driven Bayesian network approach to explore the impact of risk factors on maritime safety using a large dataset of maritime accidents. The interdependencies among risk influencing factors are modeled using a Tree Augmented Network, followed by the sensitivity analysis and model validation. The results indicate that the key risk influencing factors influencing maritime accidents mainly include ship location, type, age, gross tonnage, and deadweight tonnage. This study contributes to the prevention of specific types of maritime accidents.

UR - http://www.scopus.com/inward/record.url?scp=85195144027&partnerID=8YFLogxK

U2 - 10.1201/9781003508762-36

DO - 10.1201/9781003508762-36

M3 - Conference article in proceedings

AN - SCOPUS:85195144027

SN - 978-1-032-83099-5

SN - 978-1-032-83310-1

VL - 1

T3 - Proceedings in Marine Technology and Ocean Engineering

SP - 287

EP - 295

BT - Advances in Maritime Technology and Engineering

A2 - Soares, C. Guedes

A2 - Santos, Tiago A.

PB - CRC Press

T2 - International Conference on Maritime Technology and Engineering

Y2 - 14 May 2023 through 16 May 2023

ER -

Jiang HY, Zhang JF, Wan CP, Zhang MY, Soares CG. A data-driven Bayesian Network for risk modeling and causal analysis of global maritime accidents. julkaisussa Soares CG, Santos TA, toimittajat, Advances in Maritime Technology and Engineering. Vuosikerta 1. CRC Press. 2024. s. 287-295. (Proceedings in Marine Technology and Ocean Engineering). doi: 10.1201/9781003508762-36

A data-driven Bayesian Network for risk modeling and causal analysis of global maritime accidents

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