Application of Rough Set Theory and Bow-Tie Analysis to Maritime Safety Analysis Management: A Case Study of Taiwan Ship Collision Incidents
Abstract
:Featured Application
Abstract
1. Introduction
1.1. Background
1.2. Risk Assessment
1.3. Research Objective
2. Methodologies
2.1. Establishment of the Decision-Making Group
2.2. The Initial Set of Risk Factors
2.3. Explore the Risk Factor Criteria
2.4. Rough Set Theory (RST)
2.5. Bow-Tie Analysis (BTA)
3. Results
3.1. Representative Risk Factors
- Carelessness
- Personal improper operation
- Unfamiliarity of local navigation regulations
- Unfamiliarity of ship characteristics
- Mechanical malfunction
- Poor sea conditions
- Traffic density
3.2. Bow-Tie Risk Analysis
- Carelessness
- Personal improper operation
- Unfamiliarity with local navigation regulations
- Unfamiliarity with ship characteristics
- Mechanical malfunction
- Poor sea conditions
- Traffic density
- Injured, missing, or dead
- Marine pollution
- Damage or sinking of ships
- Operating Losses
- Other losses
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
BTA | Bow-tie analysis |
ETA | Event tree analysis |
FTA | Fault tree analysis |
FSA | Formal safety assessment |
IMO | International Maritime Organization |
IS | Information system |
ROSE2 | Rough Set Data Explorer |
RST | Rough Set Theory |
TSS | Traffic separation scheme |
References
- Civil Aeronautics Administration of the Ministry of Transportation and Communications R.O.C. Annual Report 2020. 2020. Available online: www.motcmpb.gov.tw (accessed on 1 December 2021).
- Chen, Y.H. The Risk and Prevention of Ship Collision and Maritime Safety Management. J. Taiwan Marit. Saf. Secur. Stud. 2013, 4, 1–27. [Google Scholar]
- MOTC. Maritime Accident Case. Available online: stat.motc.gov.tw/mocdb/stmain.jsp?sys=100 (accessed on 24 June 2021).
- Chen, J.; Zhang, F.; Yang, C.; Zhang, C.; Luo, L. Factor and trend analysis of total-loss marine casualty using a fuzzy matter element method. Int. J. Disaster Risk Reduct. 2017, 24, 383–390. [Google Scholar] [CrossRef]
- Eliopoulou, E.; Papanikolaou, A.; Voulgarellis, M. Statistical analysis of ship accidents and review of safety level. Saf. Sci. 2016, 85, 282–292. [Google Scholar] [CrossRef]
- Chae, C.J.; Kim, K.H.; Kang, S.Y. Limiting Ship Accidents by Identifying Their Causes and Determining Barriers to Application of Preventive Measures. J. Mar. Sci. Eng. 2021, 9, 302. [Google Scholar] [CrossRef]
- Weng, J.X.; Yang, D. Investigation of shipping accident injury severity and mortality. Accid. Anal. Prev. 2015, 76, 92–101. [Google Scholar] [CrossRef]
- Kuznecovs, A.; Ringsberg, J.W.; Ullal, A.M.; Bangera, P.J.; Johnson, E. Consequence analyses of collision-damaged ships—Damage stability, structural adequacy and oil spills. Ships Offshore Struct. 2022, 1–15. [Google Scholar] [CrossRef]
- Mou, J.M.; Van, D.; Tak, C.; Ligteringen, H. Study on collision avoidance in busy waterways by using AIS data. Ocean Eng. 2010, 37, 483–490. [Google Scholar] [CrossRef]
- Arici, S.S.; Akyuz, E.; Arslan, O. Application of fuzzy bow-tie risk analysis to maritime transportation: The case of ship collision during the STS operation. Ocean Eng. 2020, 217, 107960. [Google Scholar] [CrossRef]
- Chai, T.; Weng, J.; De-qi, X. Development of a quantitative risk assessment model for ship collisions in fairways. Saf. Sci. 2017, 91, 71–83. [Google Scholar] [CrossRef]
- Gul, M.; Celik, E.; Akyuz, E. A hybrid risk-based approach for maritime applications: The case of ballast tank maintenance. Hum. Ecol. Risk Assess. Int. J. 2017, 23, 1389–1403. [Google Scholar] [CrossRef]
- Zhang, M.; Montewka, J.; Manderbacka, T.; Kujala, P.; Hirdaris, S. A Big Data Analytics Method for the Evaluation of Ship—Ship Collision Risk reflecting Hydrometeorological Conditions. Reliab. Eng. Syst. Saf. 2021, 213, 107674. [Google Scholar] [CrossRef]
- Chen, D.; Pei, Y.; Xia, Q. Research on human factors cause chain of ship accidents based on multidimensional association rules. Ocean Eng. 2020, 218, 107717. [Google Scholar] [CrossRef]
- Martins, M.R.; Maturana, M.C. Human error contribution in collision and grounding of oil tankers. Risk Anal. 2010, 30, 674–698. [Google Scholar] [CrossRef] [PubMed]
- Zhang, M.; Zhang, D.; Goerlandt, F.; Yan, X.; Kujala, P. Use of HFACS and fault tree model for collision risk factors analysis of icebreaker assistance in ice-covered waters. Saf. Sci. 2019, 111, 128–143. [Google Scholar] [CrossRef]
- Chauvin, C.; Lardjane, S.; Morel, G.; Clostermann, J.P.; Langard, B. Human and organizational factors in maritime accidents: Analysis of collisions at sea using the HFACS. Accid. Anal. Prev. 2013, 59, 26–37. [Google Scholar] [CrossRef]
- Raiyan, A.; Das, S.; Islam, M.R. Event Tree Analysis of Marine Accidents in Bangladesh. Procedia Eng. 2017, 194, 276–283. [Google Scholar] [CrossRef]
- Kontovas, C.A.; Psaraftis, H.N. Formal Safety Assessment: A Critical Review. Mar. Technol. 2009, 46, 45–59. [Google Scholar] [CrossRef]
- Hetherington, C.; Flin, R.; Mearns, K. Safety in shipping: The human element. J. Saf. Res. 2006, 37, 401–411. [Google Scholar] [CrossRef]
- Kristiansen, S. Maritime Transportation: Safety Management and Risk Analysis; Routledge: London, UK, 2013. [Google Scholar]
- Xi, Y.T.; Hu, S.; Yang, Z.L.; Fu, S.S.; Weng, J.X. Analysis of safety climate effect on individual safety consciousness creation and safety behaviour improvement in shipping operations. Marit. Policy Manag. 2022, 1–16. [Google Scholar] [CrossRef]
- Hu, Y.; Park, G. Collision risk assessment based on the vulnerability of marine accidents using fuzzy logic. Int. J. Nav. Archit. Ocean Eng. 2020, 12, 541–551. [Google Scholar] [CrossRef]
- Wu, B.; Zhao, C.; Yip, T.Z.; Jiang, D. A novel emergency decision-making model for collision accidents in the Yangtze River. Ocean Eng. 2021, 223, 108622. [Google Scholar] [CrossRef]
- Lioa, K.C.; Wu, C.C.; Hsiao, Y.C. An Analysis of The Key Human Factor in Collision Accident on Maritime Casualty—Application Analytic Hierarchy Process. Marit. Q. 2006, 15, 67–93. [Google Scholar]
- Uğurlu, Ö.; Yıldız, S.; Loughney, S.; Wang, J. Modified human factor analysis and classification system for passenger vessel accidents (HFACS-PV). Ocean Eng. 2018, 161, 47–61. [Google Scholar] [CrossRef] [Green Version]
- Akyuz, E.; Celik, M. Utilisation of cognitive map in modelling human error in marine accident analysis and prevention. Saf. Sci. 2014, 70, 19–28. [Google Scholar] [CrossRef]
- Chen, S.T. An approach of identifying the common human and organizational factors (HOFs) among a group of marine accidents using GRA and HFACS-MA. J. Transp. Saf. Secur. 2019, 12, 1252–1294. [Google Scholar]
- Endrina, N.; Konovessis, D.; Sourina, O.; Krishnan, G. Influence of ship design and operational factors on human performance and evaluation of effects and sensitivity using risk models. Ocean Eng. 2019, 184, 143–158. [Google Scholar] [CrossRef]
- Hamouda, S.K.M.; Wahed, M.E.; Alez, R.H.A.; Riad, K. Robust breast cancer prediction system based on rough set theory at National Cancer Institute of Egypt. Comput. Methods Programs Biomed. 2018, 153, 259–268. [Google Scholar] [CrossRef]
- Bania, R.K.; Halder, A. R-HEFS: Rough set based heterogeneous ensemble feature selection method for medical data classification. Artif. Intell. Med. 2021, 14, 102049. [Google Scholar] [CrossRef] [PubMed]
- Tang, J.; Zhang, X.; Yu, T.; Liu, F. Missing traffic data imputation considering approximate intervals: A hybrid structure integrating adaptive network-based inference and fuzzy rough set. Phys. A Stat. Mech. Its Appl. 2021, 573, 125776. [Google Scholar] [CrossRef]
- Błaszczyński, J.; Filho, A.T.A.; Matuszyk, A.; Szeląg, M.; Słowiński, R. Auto loan fraud detection using dominance-based rough set approach versus machine learning methods. Expert Syst. Appl. 2021, 163, 113740. [Google Scholar] [CrossRef]
- Xu, B.; Hu, J.; Hu, T.; Wang, F.; Luo, K.; Wang, Q.; He, X. Quantitative assessment of seismic risk in hydraulic fracturing areas based on rough set and Bayesian network: A case analysis of Changning shale gas development block in Yibin City, Sichuan Province, China. J. Pet. Sci. Eng. 2021, 200, 108226. [Google Scholar] [CrossRef]
- Suo, M.; Tao, L.; Zhu, B.; Miao, X.; Liang, Z.; Ding, Y.; Zhang, X.; Zhang, T. Single-parameter decision-theoretic rough set. Inf. Sci. 2020, 539, 49–80. [Google Scholar] [CrossRef]
- Marhavilas, P.; Koulouriotis, D.; Mitrakas, C. Fault and event-tree techniques in occupational health-safety systems—Part i: Integrated risk-evaluation scheme. Environ. Eng. Manag. J. 2014, 13, 2097–2108. [Google Scholar] [CrossRef]
- Chatzimichailidou, M.M.; Karanikas, N.; Plioutsias, A. Application of STPA on small drone operations: A benchmarking approach. Procedia Eng. 2017, 179, 13–22. [Google Scholar] [CrossRef]
- Mullins, B.T.; McGurk, R.; McLeod, R.D.; Lindsay, D.; Amos, A.; Gu, D.; Chera, B.S.; Marks, L.; Das, S.; Mazur, L. Human Error Bowtie Analysis to Enhance Patient Safety in Radiation Oncology. Pract. Radiat. Oncol. 2019, 9, 465–478. [Google Scholar] [CrossRef]
- Zhao, L.; Yan, Y.; Wang, P.; Yan, X. A risk analysis model for underground gas storage well integrity failure. J. Loss Prev. Process Ind. 2019, 62, 103951. [Google Scholar] [CrossRef]
- Xie, S.; Dong, S.; Chen, Y.; Peng, Y.; Li, X. A novel risk evaluation method for fire and explosion accidents in oil depots using bow-tie analysis and risk matrix analysis method based on cloud model theory. Reliab. Eng. Syst. Saf. 2021, 215, 107791. [Google Scholar] [CrossRef]
- Cormier, R.; Elliott, M.; Rice, J. Putting on a bowtie to sort out who does what and why in the complex arena of marine policy and management. Sci. Total Environ. 2019, 648, 293–305. [Google Scholar] [CrossRef]
- Hughes, P.; Shipp, D.; Figueres-Esteban, M.; Gulijk, C. From free-text to structured safety management: Introduction of a semi-automated classification method of railway hazard reports to elements on a bow-tie diagram. Saf. Sci. 2018, 110, 11–19. [Google Scholar] [CrossRef] [Green Version]
- Purton, L.; Clothier, R.; Kourousis, K. Assessment of Technical Airworthiness in Military Aviation: Implementation and Further Advancement of the Bow-tie Model. Procedia Eng. 2014, 80, 529–544. [Google Scholar] [CrossRef] [Green Version]
- Kaptan, M. Risk assessment of ship anchorage handling operations using the fuzzy bow-tie method. Ocean Eng. 2021, 236, 109500. [Google Scholar] [CrossRef]
- Domínguez, R.; Gomez, C.; Cerezo, O. Risk Analysis Based on ETA, FTA and Bowtie Methodologies for the Bulk Coal Discharge Process. In International Conference on Applied Human Factors and Ergonomics; Springer: Cham, Switzerland, 2021; pp. 193–199. [Google Scholar]
- Sakar, C.; Buber, M.; Koseoglu, B.; Toz, A.C. Risk analysis for confined space accidents onboard ship using fuzzy bow-tie methodology. Ocean Eng. 2022, 263, 112386. [Google Scholar] [CrossRef]
- Xue, C.H.; Tang, L. Organisational support and safety management: A study of shipboard safety supervision, The Economic and Labour Relations Review. Econ. Labour Relat. Rev. 2019, 30, 103530461986957. [Google Scholar] [CrossRef]
- Karakasnaki, M.; Vlachopoulos, P.; Pantouvakis, A.; Bouranta, N. ISM Code implementation: An investigation of safety issues in the shipping industry. WMU J. Marit. Aff. 2018, 17, 461–474. [Google Scholar] [CrossRef]
- MOTC. Shipwreck Disaster Prevention and Rescue Business Plan; MOTC: Taipei City, Taiwan, 2019.
- Sotiralis, P.; Ventikos, N.P.; Hamann, P.; Golyshev, P.; Teixeira, A.P. Incorporation of human factors into ship collision risk models focusing on human centred design aspects. Reliab. Eng. Syst. Saf. 2016, 156, 210–227. [Google Scholar] [CrossRef]
- Mišković, D.; Bielić, T.; Jelena Čulin, J. Impact of Technology on Safety as Viewed by Ship Operators. Trans. Marit. Sci. 2018, 7, 51–58. [Google Scholar] [CrossRef] [Green Version]
- Morel, G.; Chauvin, C. A socio-technical approach of risk management applied to collisions involving fishing vessels. Saf. Sci. 2006, 44, 599–619. [Google Scholar] [CrossRef]
- Chen, J.; Di, Z.; Shi, J.; Shu, Y.; Wan, Z.; Song, L.; Zhang, W. Marine oil spill pollution causes and governance: A case study of Sanchi tanker collision and explosion. J. Clean. Prod. 2000, 273, 122978. [Google Scholar] [CrossRef]
- The European Marine Safety Agency (EMSA). Annual Overview of Marine Casualties and Incidents. 2021. Available online: www.emsa.europa.eu/newsroom/latest-news/item/4867-annual-overview-of-marine-casualties-and-incidents-2021.html (accessed on 16 February 2023).
- Chai, T.; Xue, H. A study on ship collision conflict prediction in the Taiwan Strait using the EMD-based LSSVM method. PLoS ONE 2021, 16, e0250948. [Google Scholar] [CrossRef]
- Weng, J.; Li, G. Exploring shipping accident contributory factors using association rules. J. Transp. Saf. Secur. 2019, 11, 36–57. [Google Scholar] [CrossRef]
- Aydin, M.; Akyuz, E.; Turan, O.; Arslan, O. Validation of risk analysis for ship collision in narrow waters by using fuzzy Bayesian networks approach. Ocean Eng. 2021, 231, 108973. [Google Scholar] [CrossRef]
- Pawlak, Z. Rough Sets. Int. J. Comput. Inf. Sci. 1982, 11, 341–356. [Google Scholar] [CrossRef]
- Guo, Y.; Tsang, E.C.; Xu, W.; Chen, D. Local logical disjunction double-quantitative rough sets. Inf. Sci. 2019, 500, 87–112. [Google Scholar] [CrossRef]
- Lin, W.T.; Wu, Y.C.; Zheng, J.S.; Chen, M.Y. Analysis by data mining in the emergency medicine triage database at a Taiwanese regional hospital. Expert Syst. Appl. 2011, 38, 11078–11084. [Google Scholar] [CrossRef]
- Shiau, T.A.; Huang, W.K. User perspective of age-friendly transportation: A case study of Taipei City. Transp. Policy 2014, 36, 184–191. [Google Scholar] [CrossRef]
- Zolin, M.B.; Ferretti, P.; Némedi, K. Multi-criteria decision approach and sustainable territorial subsystems: An Italian rural and mountain area case study. Land Use Policy 2017, 69, 598–607. [Google Scholar] [CrossRef]
- Alizadeh, S.S.; Moshashaei, P. The Bowtie method in safety management system: A literature review. Sci. J. Rev. 2015, 4, 133–138. [Google Scholar]
- Chevreau, F.R.; Wybo, J.L.; Cauchois, D. Organizing learning processes on risks by using the bow-tie representation. J. Hazard. Mater. 2006, 130, 276–283. [Google Scholar] [CrossRef]
- Saud, Y.E.; Israni, K.; Goddard, J. Bow-tie diagrams in downstream hazard identification and risk assessment. Process Saf. Prog. 2014, 33, 26–35. [Google Scholar] [CrossRef]
- Cormier, R.; Stelzenmüller, V.; Creed, I.F.; Igras, J.; Rambo, H.; Callies, C.; Johnson, L.B. The science-policy interface of risk-based freshwater and marine management systems: From concepts to practical tools. J. Environ. Manag. 2018, 226, 340–346. [Google Scholar] [CrossRef]
- Cormier, R.; Londsdale, J. Risk assessment for deep sea mining: An overview of risk. Mar. Policy 2020, 114, 103485. [Google Scholar] [CrossRef]
- Dominguez-Péry, C.; Vuddaraju, L.N.R.; Corbett-Etchevers, I.; Tassabehji, R. Reducing maritime accidents in ships by tackling human error: A bibliometric review and research agenda. J. Shipp. Trade 2021, 6, 20. [Google Scholar] [CrossRef]
- Lützhöft, M.; Grech, M.R.; Porathe, T. Information environment, fatigue, and culture in the maritime domain. Rev. Hum. Factors Ergon. 2011, 7, 280–322. [Google Scholar] [CrossRef]
- Zhao, X.Y.; He, Y.X.; Huang, L.W.; Mou, J.M.; Zhang, K.; Liu, X. Intelligent Collision Avoidance Method for Ships ased on COLRGEs and Improved Velocity Obstacle Algorithm. Appl. Sci. 2022, 12, 8926. [Google Scholar] [CrossRef]
- Zheng, Y.; Zhang, X.; Shang, Z.; Guo, S.; Du, Y. A Decision-Making Method for Ship Collision Avoidance Based on Improved Cultural Particle Swarm. J. Adv. Transp. 2021, 2021, 8898507. [Google Scholar] [CrossRef]
- Karahalios, H. The contribution of risk management in ship management: The case of ship collision. Saf. Sci. 2014, 63, 104–114. [Google Scholar] [CrossRef]
- Shen, J.H.; Liu, C.P.; Chang, K.Y.; Chen, Y.W. Ship Deficiency Data of Port State Control to Identify Hidden Risk of Target Ship. J. Mar. Sci. Eng. 2021, 9, 1120. [Google Scholar] [CrossRef]
- Mišković, D.; Ivče, R.; Hess, M.; Koboević, Ž. The Influence of Shipboard Safety Factors on Quality of Safety Supervision: Croatian Seafarer’s Attitudes. J. Mar. Sci. Eng. 2022, 10, 1265. [Google Scholar] [CrossRef]
- Fiskin, R.; Atik, O.; Kisi, H.; Nasibov, E.; Johansen, T.A. Fuzzy domain and meta-heuristic algorithm-based collision avoidance control for ships: Experimental validation in virtual and real environment. Ocean Eng. 2021, 220, 108502. [Google Scholar] [CrossRef]
- Shen, H.; Hashimoto, H.; Matsuda, A.; Taniguchi, A.; Terada, D.; Guo, C. Automatic collision avoidance of multiple ships based on deep Q-learning. Appl. Ocean Res. 2019, 86, 268–288. [Google Scholar] [CrossRef]
- Fan, L.; Wang, M.; Yin, J. The impacts of risk level based on PSC inspection deficiencies on ship accident consequences. Res. Transp. Bus. Manag. 2019, 33, 100464. [Google Scholar] [CrossRef]
- IMO resolution, A. 1052. Procedures for Port State Control; International Maritime Organization: London, UK, 2011. [Google Scholar]
- Paris MoU on Port State Control, White, Grey and Black List. Available online: www.parismou.org/detentions-banning/white-grey-and-black-list (accessed on 15 March 2023).
- Tokyo MoU, Black–Grey–White Lists. Available online: www.tokyo-mou.org (accessed on 19 March 2023).
- Zhang, P.F.; Edward, P. Safety first: Reconstructing the concept of seaworthiness under the maritime labour convention 2006. Mar. Policy 2016, 67, 54–59. [Google Scholar] [CrossRef]
- Chen, P.; Mou, J.; Gelder, P.H. Integration of individual encounter information into causation probability modelling of ship collision accidents. Saf. Sci. 2019, 120, 636–651. [Google Scholar] [CrossRef]
- Du, L.; Goerland, F.; Kujala, P. Review and analysis of methods for assessing maritime waterway risk based on non-accident critical events detected from AIS data. Reliab. Eng. Syst. Saf. 2020, 200, 106933. [Google Scholar] [CrossRef]
- Liu, Z.; Wu, Z.; Zheng, Z. A novel framework for regional collision risk identification based on AIS data. Appl. Ocean Res. 2019, 89, 261–272. [Google Scholar] [CrossRef]
- Mou, J.M.; Zhou, C.; Tang, W.M. Evaluate VTS benefits: A case study of Zhoushan Port. Int. J. e-Navig. Marit. Econ. 2015, 3, 22–31. [Google Scholar] [CrossRef] [Green Version]
- Oh, J.H.; Kim, K.; Jeong, J.S. A Study on the Risk Analysis based on the Trajectory of Fishing Vessels in the VTS Area. Int. J. e-Navig. Marit. Econ. 2015, 2, 38–46. [Google Scholar] [CrossRef] [Green Version]
No. | Position | Education | Professional Field | Maritime Experience in Years |
---|---|---|---|---|
1 | Captain of fishing boat | Senior high school | Navigation and management | 38 |
2 | Captain of fishing boat | Senior high school | Navigation and management | 35 |
3 | Captain of merchant ship | Master’s degree | Navigation and management | 31 |
4 | Professor | Doctorate | Safety management | 20 |
5 | Pilot | Master’s degree | Navigation and navigation safety | 30 |
6 | Manager | Bachelor’s degree | Management | 16 |
Category | Factors | References |
---|---|---|
A: Human-made | A1: Personal physical illness | [2,17] |
A2: Absence-mindedness | [2,17,22,23,24,47,48] | |
A3: Personal alcohol and drug habits | [17,25,26] | |
A4: Stressed and nervous | [22,25,49,50,51] | |
A5: Carelessness | [17,52] | |
A6: Personal improper operation | [17,22,49,53] | |
A7: Personal misjudgments | [26,50,53] | |
A8: Unfamiliarity with local navigation rules | [49,53] | |
A9: Unfamiliarity with COLREGS | [26,51,52] | |
A10: Unwillingness to avoidance | [11,49,51,53] | |
A11: Insufficient avoidance skills | [49,50] | |
A12: Violation of regulations on duty | [11,17,52,53] | |
A13: Incompatible communication of avoidance | [17,52,53] | |
A14: Unfamiliarity of ship characteristics | [24,25,29,51] | |
A15: Inaccurate maintenance | [25,49,54] | |
B: Ship | B1: Irregular maintenance | [25,49,54] |
B2: Insufficient equipment | [26,52,54] | |
B3: Mechanical malfunction | [26,49] | |
B4: Old ship | [11,49,53] | |
B5: Ship characteristics | [17,22,25,26,47,48] | |
B6: Cargo overloaded | [25,55] | |
B7: Poor working environment | [11,25,53] | |
B8: Local port management | [17,49] | |
B9: Crew/labor shortage | [26,52] | |
C: Environment | C1: Poor sea conditions | [23,50] |
C2: Sudden changes of weather | [23,25] | |
C3: Dense fog and heavy rain | [17,52,56] | |
C4: Narrow channel | [17,23,57] | |
C5: Traffic density | [10,23,25,52] |
Risk Factor | Description |
---|---|
Relevance | The measurement scales were divided into high relevance (H), medium relevance (M), and low relevance (L), which were based on the correlations between risk factors and ship collisions. |
Influence level | The level of influence between risk factors and the ship collision incident was used mainly to measure the severity of the consequences of the ship collision. The factor with a higher degree of influence level would lead to a larger impact on a ship collision. The measurement scale included high influence (H), medium influence (M), and low influence (L). |
Incidence rate | To effectively prevent the occurrence of ship collisions, the incidence rate of risk factors can accurately determine the incidence rate of ship collisions. The measurement scales were divided into high incidence rate (H), medium incidence rate (M), and low incidence rate (L). |
Representativeness | The causes of ship collisions included human-made, ship, and environmental aspects, and the relevance, degree of influence, and incidence of each risk factor were also different. Therefore, the selection of risk factors should be examined for whether they were representative enough. The measurement scale was classified as high representativeness (H), medium representativeness (M), and low representativeness (L). |
No. | Factors | Relevance | Influence Level | Incidence Rate | Representativeness |
---|---|---|---|---|---|
1 | A1 | L | L | M | M |
2 | A2 | M | M | H | M |
3 | A3 | H | M | M | M |
4 | A4 | M | M | M | M |
5 | A5 | H | H | H | H |
6 | A6 | H | H | H | H |
7 | A7 | M | H | H | M |
8 | A8 | H | H | H | H |
9 | A9 | H | M | H | M |
10 | A10 | H | H | M | M |
11 | A11 | M | M | H | H |
12 | A12 | H | H | M | M |
13 | A13 | H | M | H | H |
14 | A14 | H | H | H | H |
15 | A15 | M | M | H | M |
16 | B1 | M | M | H | H |
17 | B2 | M | H | H | M |
18 | B3 | H | H | H | H |
19 | B4 | H | L | M | M |
20 | B5 | H | M | M | M |
21 | B6 | M | H | H | H |
22 | B7 | L | M | L | L |
23 | B8 | M | M | M | M |
24 | B9 | H | H | M | M |
25 | C1 | H | H | H | H |
26 | C2 | H | M | H | M |
27 | C3 | M | M | H | H |
28 | C4 | M | M | H | H |
29 | C5 | H | H | H | H |
RF1 | RF2 | RF3 | RF4 | Corresponding Risk Factors | |
---|---|---|---|---|---|
Rule 1 | L | L | 22 | ||
Rule 2 | M | M | 1, 3, 4, 10, 12, 19, 20, 23, 24 | ||
Rule 3 | H | H | H | H | 5, 6, 8, 14, 18, 25, 29 |
Rule 4 | M | H | M | 2, 7, 15, 17 | |
H | 11, 16, 21, 27, 28 | ||||
Rule 5 | M | H | M | 2, 9, 15, 26 | |
H | 11, 13, 16, 27, 28 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Hsu, S.-H.; Lee, M.-T.; Chang, Y.-C. Application of Rough Set Theory and Bow-Tie Analysis to Maritime Safety Analysis Management: A Case Study of Taiwan Ship Collision Incidents. Appl. Sci. 2023, 13, 4239. https://doi.org/10.3390/app13074239
Hsu S-H, Lee M-T, Chang Y-C. Application of Rough Set Theory and Bow-Tie Analysis to Maritime Safety Analysis Management: A Case Study of Taiwan Ship Collision Incidents. Applied Sciences. 2023; 13(7):4239. https://doi.org/10.3390/app13074239
Chicago/Turabian StyleHsu, Shao-Hua, Meng-Tsung Lee, and Yang-Chi Chang. 2023. "Application of Rough Set Theory and Bow-Tie Analysis to Maritime Safety Analysis Management: A Case Study of Taiwan Ship Collision Incidents" Applied Sciences 13, no. 7: 4239. https://doi.org/10.3390/app13074239
APA StyleHsu, S. -H., Lee, M. -T., & Chang, Y. -C. (2023). Application of Rough Set Theory and Bow-Tie Analysis to Maritime Safety Analysis Management: A Case Study of Taiwan Ship Collision Incidents. Applied Sciences, 13(7), 4239. https://doi.org/10.3390/app13074239