A Fuzzy Rule-Based Ship Risk Profile Prediction Model for Port State Control Inspections

S. M.Esad Demirci; Kadir Cicek; Ulku Ozturk

doi:10.1007/978-3-030-85577-2_59

A Fuzzy Rule-Based Ship Risk Profile Prediction Model for Port State Control Inspections

S. M.Esad Demirci^*, Kadir Cicek, Ulku Ozturk

^*Corresponding author for this work

Department of Marine Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

Maritime transport is the backbone of the international trade, more than 80% of global freight transport is carried by ships on the seas. However, in a complex and high-risk environment at sea, substandard ships in maritime transport causes serious accidents and hence bring out many threats to the maritime industry. As a result, with the aim of detection and elimination the substandard ships, port state controls (PSC) have been developed to inspect the ships respect to their Ship Risk Profile (SRP). SRP helps to determine the ship’s priority for PSC inspections through categorizing the ships in high risk, standard risk or low risk using various generic and historic parameters. In this study a new model is proposed to predict SRP by using fuzzy clustering analysis (FCA) and fuzzy rule-based classification system (FRBS) different from the standard calculation of the SRP in the PSC regimes. The proposed model is structured on five different parameters which are ship type, ship flag, ship age, deficiency number and detention. In the proposed model, to predict the SRP, 53788 ship inspection data belonging to the parameters has been analyzed gathered from the Paris MoU online database between the years of 2017 and 2020. The results obtained with the proposed approach help to identify the risk profile for the ship targeted at almost certain risk level. As a result of the study, it is aimed to provide decision supports for port state control (PSC) officers to detect the most appropriate/risky ship for the inspection.

Original language	English
Title of host publication	Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation - Proceedings of the INFUS 2021 Conference
Editors	Cengiz Kahraman, Selcuk Cebi, Sezi Cevik Onar, Basar Oztaysi, A. Cagri Tolga, Irem Ucal Sari
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	498-505
Number of pages	8
ISBN (Print)	9783030855765
DOIs	https://doi.org/10.1007/978-3-030-85577-2_59
Publication status	Published - 2022
Event	International Conference on Intelligent and Fuzzy Systems, INFUS 2021 - Istanbul, Turkey Duration: 24 Aug 2021 → 26 Aug 2021

Publication series

Name	Lecture Notes in Networks and Systems
Volume	308
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	International Conference on Intelligent and Fuzzy Systems, INFUS 2021
Country/Territory	Turkey
City	Istanbul
Period	24/08/21 → 26/08/21

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Funding

The article is produced from PhD thesis research of S. M. Esad DEMIRCI entitled “Safety Based Intelligent Ship Inspection Analytics for Maritime Transportation” which has been executed in a PhD Program in Maritime Transportation Engineering of Istanbul Technical University Graduate School.

Funders	Funder number
Istanbul Teknik Üniversitesi

Keywords

Fuzzy Clustering Analysis (FCA)
Fuzzy Rule-Based Classification System (FRBS)
Port State Control (PSC) Inspections
Ship Risk Profile (SRP)

Access to Document

10.1007/978-3-030-85577-2_59

Cite this

Demirci, S. M. E., Cicek, K., & Ozturk, U. (2022). A Fuzzy Rule-Based Ship Risk Profile Prediction Model for Port State Control Inspections. In C. Kahraman, S. Cebi, S. Cevik Onar, B. Oztaysi, A. C. Tolga, & I. U. Sari (Eds.), Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation - Proceedings of the INFUS 2021 Conference (pp. 498-505). (Lecture Notes in Networks and Systems; Vol. 308). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-85577-2_59

Demirci, S. M.Esad ; Cicek, Kadir ; Ozturk, Ulku. / A Fuzzy Rule-Based Ship Risk Profile Prediction Model for Port State Control Inspections. Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation - Proceedings of the INFUS 2021 Conference. editor / Cengiz Kahraman ; Selcuk Cebi ; Sezi Cevik Onar ; Basar Oztaysi ; A. Cagri Tolga ; Irem Ucal Sari. Springer Science and Business Media Deutschland GmbH, 2022. pp. 498-505 (Lecture Notes in Networks and Systems).

@inproceedings{212cde32f03b45dcb72dfc4fb6aaf42b,

title = "A Fuzzy Rule-Based Ship Risk Profile Prediction Model for Port State Control Inspections",

abstract = "Maritime transport is the backbone of the international trade, more than 80% of global freight transport is carried by ships on the seas. However, in a complex and high-risk environment at sea, substandard ships in maritime transport causes serious accidents and hence bring out many threats to the maritime industry. As a result, with the aim of detection and elimination the substandard ships, port state controls (PSC) have been developed to inspect the ships respect to their Ship Risk Profile (SRP). SRP helps to determine the ship{\textquoteright}s priority for PSC inspections through categorizing the ships in high risk, standard risk or low risk using various generic and historic parameters. In this study a new model is proposed to predict SRP by using fuzzy clustering analysis (FCA) and fuzzy rule-based classification system (FRBS) different from the standard calculation of the SRP in the PSC regimes. The proposed model is structured on five different parameters which are ship type, ship flag, ship age, deficiency number and detention. In the proposed model, to predict the SRP, 53788 ship inspection data belonging to the parameters has been analyzed gathered from the Paris MoU online database between the years of 2017 and 2020. The results obtained with the proposed approach help to identify the risk profile for the ship targeted at almost certain risk level. As a result of the study, it is aimed to provide decision supports for port state control (PSC) officers to detect the most appropriate/risky ship for the inspection.",

keywords = "Fuzzy Clustering Analysis (FCA), Fuzzy Rule-Based Classification System (FRBS), Port State Control (PSC) Inspections, Ship Risk Profile (SRP)",

author = "Demirci, {S. M.Esad} and Kadir Cicek and Ulku Ozturk",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; International Conference on Intelligent and Fuzzy Systems, INFUS 2021 ; Conference date: 24-08-2021 Through 26-08-2021",

year = "2022",

doi = "10.1007/978-3-030-85577-2_59",

language = "English",

isbn = "9783030855765",

series = "Lecture Notes in Networks and Systems",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "498--505",

editor = "Cengiz Kahraman and Selcuk Cebi and {Cevik Onar}, Sezi and Basar Oztaysi and Tolga, {A. Cagri} and Sari, {Irem Ucal}",

booktitle = "Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation - Proceedings of the INFUS 2021 Conference",

address = "Germany",

}

Demirci, SME, Cicek, K & Ozturk, U 2022, A Fuzzy Rule-Based Ship Risk Profile Prediction Model for Port State Control Inspections. in C Kahraman, S Cebi, S Cevik Onar, B Oztaysi, AC Tolga & IU Sari (eds), Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation - Proceedings of the INFUS 2021 Conference. Lecture Notes in Networks and Systems, vol. 308, Springer Science and Business Media Deutschland GmbH, pp. 498-505, International Conference on Intelligent and Fuzzy Systems, INFUS 2021, Istanbul, Turkey, 24/08/21. https://doi.org/10.1007/978-3-030-85577-2_59

A Fuzzy Rule-Based Ship Risk Profile Prediction Model for Port State Control Inspections. / Demirci, S. M.Esad; Cicek, Kadir; Ozturk, Ulku.
Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation - Proceedings of the INFUS 2021 Conference. ed. / Cengiz Kahraman; Selcuk Cebi; Sezi Cevik Onar; Basar Oztaysi; A. Cagri Tolga; Irem Ucal Sari. Springer Science and Business Media Deutschland GmbH, 2022. p. 498-505 (Lecture Notes in Networks and Systems; Vol. 308).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A Fuzzy Rule-Based Ship Risk Profile Prediction Model for Port State Control Inspections

AU - Demirci, S. M.Esad

AU - Cicek, Kadir

AU - Ozturk, Ulku

PY - 2022

Y1 - 2022

N2 - Maritime transport is the backbone of the international trade, more than 80% of global freight transport is carried by ships on the seas. However, in a complex and high-risk environment at sea, substandard ships in maritime transport causes serious accidents and hence bring out many threats to the maritime industry. As a result, with the aim of detection and elimination the substandard ships, port state controls (PSC) have been developed to inspect the ships respect to their Ship Risk Profile (SRP). SRP helps to determine the ship’s priority for PSC inspections through categorizing the ships in high risk, standard risk or low risk using various generic and historic parameters. In this study a new model is proposed to predict SRP by using fuzzy clustering analysis (FCA) and fuzzy rule-based classification system (FRBS) different from the standard calculation of the SRP in the PSC regimes. The proposed model is structured on five different parameters which are ship type, ship flag, ship age, deficiency number and detention. In the proposed model, to predict the SRP, 53788 ship inspection data belonging to the parameters has been analyzed gathered from the Paris MoU online database between the years of 2017 and 2020. The results obtained with the proposed approach help to identify the risk profile for the ship targeted at almost certain risk level. As a result of the study, it is aimed to provide decision supports for port state control (PSC) officers to detect the most appropriate/risky ship for the inspection.

AB - Maritime transport is the backbone of the international trade, more than 80% of global freight transport is carried by ships on the seas. However, in a complex and high-risk environment at sea, substandard ships in maritime transport causes serious accidents and hence bring out many threats to the maritime industry. As a result, with the aim of detection and elimination the substandard ships, port state controls (PSC) have been developed to inspect the ships respect to their Ship Risk Profile (SRP). SRP helps to determine the ship’s priority for PSC inspections through categorizing the ships in high risk, standard risk or low risk using various generic and historic parameters. In this study a new model is proposed to predict SRP by using fuzzy clustering analysis (FCA) and fuzzy rule-based classification system (FRBS) different from the standard calculation of the SRP in the PSC regimes. The proposed model is structured on five different parameters which are ship type, ship flag, ship age, deficiency number and detention. In the proposed model, to predict the SRP, 53788 ship inspection data belonging to the parameters has been analyzed gathered from the Paris MoU online database between the years of 2017 and 2020. The results obtained with the proposed approach help to identify the risk profile for the ship targeted at almost certain risk level. As a result of the study, it is aimed to provide decision supports for port state control (PSC) officers to detect the most appropriate/risky ship for the inspection.

KW - Fuzzy Clustering Analysis (FCA)

KW - Fuzzy Rule-Based Classification System (FRBS)

KW - Port State Control (PSC) Inspections

KW - Ship Risk Profile (SRP)

UR - http://www.scopus.com/inward/record.url?scp=85115254347&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-85577-2_59

DO - 10.1007/978-3-030-85577-2_59

M3 - Conference contribution

AN - SCOPUS:85115254347

SN - 9783030855765

T3 - Lecture Notes in Networks and Systems

SP - 498

EP - 505

BT - Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation - Proceedings of the INFUS 2021 Conference

A2 - Kahraman, Cengiz

A2 - Cebi, Selcuk

A2 - Cevik Onar, Sezi

A2 - Oztaysi, Basar

A2 - Tolga, A. Cagri

A2 - Sari, Irem Ucal

PB - Springer Science and Business Media Deutschland GmbH

T2 - International Conference on Intelligent and Fuzzy Systems, INFUS 2021

Y2 - 24 August 2021 through 26 August 2021

ER -

Demirci SME, Cicek K, Ozturk U. A Fuzzy Rule-Based Ship Risk Profile Prediction Model for Port State Control Inspections. In Kahraman C, Cebi S, Cevik Onar S, Oztaysi B, Tolga AC, Sari IU, editors, Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation - Proceedings of the INFUS 2021 Conference. Springer Science and Business Media Deutschland GmbH. 2022. p. 498-505. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-030-85577-2_59

A Fuzzy Rule-Based Ship Risk Profile Prediction Model for Port State Control Inspections

Abstract

Publication series

Conference

Bibliographical note

Funding

Keywords

Access to Document

Other files and links

Fingerprint

Cite this