A machine learning method for the evaluation of probabilistic grounding risk reflecting ship motion uncertainties

Mingyang Zhang; Pentti Kujala; Spyros Hirdaris

doi:10.1201/9781003462170-15

A machine learning method for the evaluation of probabilistic grounding risk reflecting ship motion uncertainties

Mingyang Zhang, Pentti Kujala, Spyros Hirdaris

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Chapter › Scientific › vertaisarvioitu

1 Sitaatiot (Scopus)

Abstrakti

The paper introduces a machine learning method for the evaluation of probabilistic grounding risk accounting for ship motion uncertainties in real operational conditions. The approach makes use of big data from Automatic Identification System (AIS), nowcast and gridded bathymetry (GEBCO) data. A machine learning method that combines Principal Component Analysis (PCA) with Multiple-Output Gaussian Process Regression (MOGPR) methods is used to predict selected features of ship motion dynamics namely ship sway, surge accelerations and drift angles. Predicted ship motion trajectories are quantified to present ship position distribution functions in the time domain using a Gaussian Progress Regression (GPR) flow method while the dynamic safety contours of bathymetry maps are extracted based on the ship’s draft and Under Keel Clearance (UKC). The method is applied for probabilistic grounding risk evaluation of a Ro-Pax ship operating between two ports in the Gulf of Finland. The results demonstrate that the presented methodology can predict probabilistic grounding risk reflecting ship motion uncertainties. Thus, it may assist with the development of novel ship decision support systems for proactive grounding risk mitigation.

Alkuperäiskieli	Englanti
Otsikko	Advances in the Collision and Grounding of Ships and Offshore Structures - Proceedings of the 9th International Conference on Collision and Grounding of Ships and Offshore Structures, ICCGS 2023
Alaotsikko	Proceedings of the 9th International Conference on Collision and Grounding of Ships and Offshore Structures
Toimittajat	Herve La Sourne, Carlos Guedes Soares
Kustantaja	CRC Press
Sivut	101-109
Sivumäärä	9
ISBN (elektroninen)	978-1-003-46217-0
ISBN (painettu)	978-1-032-61130-3
DOI - pysyväislinkit	https://doi.org/10.1201/9781003462170-15
Tila	Julkaistu - 2024
OKM-julkaisutyyppi	A3 Kirjan tai muun kokoomateoksen osa
Tapahtuma	International Conference on Collision and Grounding of Ships and Offshore Structures - Institut Catholique d'Arts et Métiers, Nantes, Ranska Kesto: 11 syysk. 2023 → 13 syysk. 2023 Konferenssinumero: 9

Julkaisusarja

Nimi	Proceedings in Marine Technology and Ocean Engineering
Kustantaja	CRC Press (Taylor and Francis Group)
Vuosikerta	12
ISSN (painettu)	2638-647X
ISSN (elektroninen)	2638-6461

Conference

Conference	International Conference on Collision and Grounding of Ships and Offshore Structures
Lyhennettä	ICCGS
Maa/Alue	Ranska
Kaupunki	Nantes
Ajanjakso	11/09/2023 → 13/09/2023

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10.1201/9781003462170-15

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Zhang, M., Kujala, P., & Hirdaris, S. (2024). A machine learning method for the evaluation of probabilistic grounding risk reflecting ship motion uncertainties. teoksessa H. La Sourne, & C. Guedes Soares (Toimittajat), Advances in the Collision and Grounding of Ships and Offshore Structures - Proceedings of the 9th International Conference on Collision and Grounding of Ships and Offshore Structures, ICCGS 2023: Proceedings of the 9th International Conference on Collision and Grounding of Ships and Offshore Structures (Sivut 101-109). (Proceedings in Marine Technology and Ocean Engineering; Vuosikerta 12). CRC Press. https://doi.org/10.1201/9781003462170-15

Zhang, Mingyang ; Kujala, Pentti ; Hirdaris, Spyros. / A machine learning method for the evaluation of probabilistic grounding risk reflecting ship motion uncertainties. Advances in the Collision and Grounding of Ships and Offshore Structures - Proceedings of the 9th International Conference on Collision and Grounding of Ships and Offshore Structures, ICCGS 2023: Proceedings of the 9th International Conference on Collision and Grounding of Ships and Offshore Structures. Toimittaja / Herve La Sourne ; Carlos Guedes Soares. CRC Press, 2024. Sivut 101-109 (Proceedings in Marine Technology and Ocean Engineering).

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title = "A machine learning method for the evaluation of probabilistic grounding risk reflecting ship motion uncertainties",

abstract = "The paper introduces a machine learning method for the evaluation of probabilistic grounding risk accounting for ship motion uncertainties in real operational conditions. The approach makes use of big data from Automatic Identification System (AIS), nowcast and gridded bathymetry (GEBCO) data. A machine learning method that combines Principal Component Analysis (PCA) with Multiple-Output Gaussian Process Regression (MOGPR) methods is used to predict selected features of ship motion dynamics namely ship sway, surge accelerations and drift angles. Predicted ship motion trajectories are quantified to present ship position distribution functions in the time domain using a Gaussian Progress Regression (GPR) flow method while the dynamic safety contours of bathymetry maps are extracted based on the ship{\textquoteright}s draft and Under Keel Clearance (UKC). The method is applied for probabilistic grounding risk evaluation of a Ro-Pax ship operating between two ports in the Gulf of Finland. The results demonstrate that the presented methodology can predict probabilistic grounding risk reflecting ship motion uncertainties. Thus, it may assist with the development of novel ship decision support systems for proactive grounding risk mitigation.",

author = "Mingyang Zhang and Pentti Kujala and Spyros Hirdaris",

year = "2024",

doi = "10.1201/9781003462170-15",

language = "English",

isbn = "978-1-032-61130-3",

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editor = "{La Sourne}, Herve and {Guedes Soares}, Carlos",

booktitle = "Advances in the Collision and Grounding of Ships and Offshore Structures - Proceedings of the 9th International Conference on Collision and Grounding of Ships and Offshore Structures, ICCGS 2023",

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note = "International Conference on Collision and Grounding of Ships and Offshore Structures , ICCGS ; Conference date: 11-09-2023 Through 13-09-2023",

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Zhang, M , Kujala, P & Hirdaris, S 2024, A machine learning method for the evaluation of probabilistic grounding risk reflecting ship motion uncertainties. julkaisussa H La Sourne & C Guedes Soares (toim), Advances in the Collision and Grounding of Ships and Offshore Structures - Proceedings of the 9th International Conference on Collision and Grounding of Ships and Offshore Structures, ICCGS 2023: Proceedings of the 9th International Conference on Collision and Grounding of Ships and Offshore Structures. Proceedings in Marine Technology and Ocean Engineering, Vuosikerta 12, CRC Press, Sivut 101-109, International Conference on Collision and Grounding of Ships and Offshore Structures , Nantes, Ranska, 11/09/2023. https://doi.org/10.1201/9781003462170-15

A machine learning method for the evaluation of probabilistic grounding risk reflecting ship motion uncertainties. / Zhang, Mingyang ; Kujala, Pentti; Hirdaris, Spyros.
Advances in the Collision and Grounding of Ships and Offshore Structures - Proceedings of the 9th International Conference on Collision and Grounding of Ships and Offshore Structures, ICCGS 2023: Proceedings of the 9th International Conference on Collision and Grounding of Ships and Offshore Structures. toim. / Herve La Sourne; Carlos Guedes Soares. CRC Press, 2024. s. 101-109 (Proceedings in Marine Technology and Ocean Engineering; Vuosikerta 12).

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Chapter › Scientific › vertaisarvioitu

TY - CHAP

T1 - A machine learning method for the evaluation of probabilistic grounding risk reflecting ship motion uncertainties

AU - Zhang, Mingyang

AU - Kujala, Pentti

AU - Hirdaris, Spyros

N1 - Conference code: 9

PY - 2024

Y1 - 2024

N2 - The paper introduces a machine learning method for the evaluation of probabilistic grounding risk accounting for ship motion uncertainties in real operational conditions. The approach makes use of big data from Automatic Identification System (AIS), nowcast and gridded bathymetry (GEBCO) data. A machine learning method that combines Principal Component Analysis (PCA) with Multiple-Output Gaussian Process Regression (MOGPR) methods is used to predict selected features of ship motion dynamics namely ship sway, surge accelerations and drift angles. Predicted ship motion trajectories are quantified to present ship position distribution functions in the time domain using a Gaussian Progress Regression (GPR) flow method while the dynamic safety contours of bathymetry maps are extracted based on the ship’s draft and Under Keel Clearance (UKC). The method is applied for probabilistic grounding risk evaluation of a Ro-Pax ship operating between two ports in the Gulf of Finland. The results demonstrate that the presented methodology can predict probabilistic grounding risk reflecting ship motion uncertainties. Thus, it may assist with the development of novel ship decision support systems for proactive grounding risk mitigation.

AB - The paper introduces a machine learning method for the evaluation of probabilistic grounding risk accounting for ship motion uncertainties in real operational conditions. The approach makes use of big data from Automatic Identification System (AIS), nowcast and gridded bathymetry (GEBCO) data. A machine learning method that combines Principal Component Analysis (PCA) with Multiple-Output Gaussian Process Regression (MOGPR) methods is used to predict selected features of ship motion dynamics namely ship sway, surge accelerations and drift angles. Predicted ship motion trajectories are quantified to present ship position distribution functions in the time domain using a Gaussian Progress Regression (GPR) flow method while the dynamic safety contours of bathymetry maps are extracted based on the ship’s draft and Under Keel Clearance (UKC). The method is applied for probabilistic grounding risk evaluation of a Ro-Pax ship operating between two ports in the Gulf of Finland. The results demonstrate that the presented methodology can predict probabilistic grounding risk reflecting ship motion uncertainties. Thus, it may assist with the development of novel ship decision support systems for proactive grounding risk mitigation.

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UR - http://www.scopus.com/inward/record.url?scp=85175821579&partnerID=8YFLogxK

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DO - 10.1201/9781003462170-15

M3 - Chapter

SN - 978-1-032-61130-3

T3 - Proceedings in Marine Technology and Ocean Engineering

SP - 101

EP - 109

BT - Advances in the Collision and Grounding of Ships and Offshore Structures - Proceedings of the 9th International Conference on Collision and Grounding of Ships and Offshore Structures, ICCGS 2023

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A2 - Guedes Soares, Carlos

PB - CRC Press

T2 - International Conference on Collision and Grounding of Ships and Offshore Structures

Y2 - 11 September 2023 through 13 September 2023

ER -

Zhang M , Kujala P, Hirdaris S. A machine learning method for the evaluation of probabilistic grounding risk reflecting ship motion uncertainties. julkaisussa La Sourne H, Guedes Soares C, toimittajat, Advances in the Collision and Grounding of Ships and Offshore Structures - Proceedings of the 9th International Conference on Collision and Grounding of Ships and Offshore Structures, ICCGS 2023: Proceedings of the 9th International Conference on Collision and Grounding of Ships and Offshore Structures. CRC Press. 2024. s. 101-109. (Proceedings in Marine Technology and Ocean Engineering). doi: 10.1201/9781003462170-15

A machine learning method for the evaluation of probabilistic grounding risk reflecting ship motion uncertainties

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