Compression of the Variables Classifying Domestic Marine Accident Data
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Maritime accidents result in enormous economic loss and loss of life; thus, such accidents must be prevented, and risks must be managed to prevent these occurrences Risk management must be based on statistical evidence such as variables. Because calculating when variables increase statistically can be difficult, compressing the designated variables is necessary to use the maritime accident data in Korea. Thus, in this study, variables of marine accident data are compressed using statistical methods. The date, ship type, and marine accident type included in all maritime accident data were extracted, the number of optimal variables was confirmed using the hierarchical clustering analysis method, and the data were compressed. For the compressed variables, the validity of the data use was statistically confirmed using analysis of variance, and the data of the variables identified using the variable compression method were designated. Consequently, among the monthly and yearly data, statistical significance was confirmed in yearly data, and compression was possible. The significance of the data was confirmed in six and eight types of ships and accidents, respectively, and these were compressed. These results can be directly used for prevention or prediction based on past maritime accident data. Additionally, the data range extracted from past maritime accidents and the number of applicable data will be studied in the future.
Abstract
Maritime accidents result in enormous economic loss and loss of life; thus, such accidents must be prevented, and risks must be managed to prevent these occurrences Risk management must be based on statistical evidence such as variables. Because calculating when variables increase statistically can be difficult, compressing the designated variables is necessary to use the maritime accident data in Korea. Thus, in this study, variables of marine accident data are compressed using statistical methods. The date, ship type, and marine accident type included in all maritime accident data were extracted, the number of optimal variables was confirmed using the hierarchical clustering analysis method, and the data were compressed. For the compressed variables, the validity of the data use was statistically confirmed using analysis of variance, and the data of the variables identified using the variable compression method were designated. Consequently, among the monthly and yearly data, statistical significance was confirmed in yearly data, and compression was possible. The significance of the data was confirmed in six and eight types of ships and accidents, respectively, and these were compressed. These results can be directly used for prevention or prediction based on past maritime accident data. Additionally, the data range extracted from past maritime accidents and the number of applicable data will be studied in the future.
참고문헌 (22)
- Abbassinia, M., "Dynamic human error assessment in emergency using fuzzy bayesian cream", Journal of Research in Health Sciences, 20(1), pp.e00468~ (2020)
- Bradley P., "baysian models for categorical data" (2011)
- Bowo, L. P., "Applied Mechanics and Materials, Vol. 874", pp.199~206 (2018)
- Cacciabue, P. C., "Human error risk management for engineering systems: A methodology for design, safety assessment, accident investigation and training", Reliability Engineering System and Safety, 83, pp.229~240 (2004)
- Chauvin, C., "Human and organisational factors in maritime accidents: Analysis of collisions at sea using the HFACS", Accident Analysis and Prevention, 59, pp.26~37 (2013)
- Cilibrasi, R., "Clustering by compression", EEE Transactions on Information theory, 51(4), pp.1523~1545 (2005)
- Friedman, N., "Bayesian network classifiers", Machine learning, 29(2), pp.131~163 (1997)
- Formal Safety Assessment, Report on the Joint MSC, "MEPC Working Group on the Human Element and Formal Safety Assessment", IMO, 2001, MSC 74[DOI]
- Kaplan, S., "On the quantitative definition of risk", Risk analysis, 1(1), pp.11~27 (1981)
- Kirwan, B., "Human error identification in human reliability assessment", Applied ergonomics, 23(5), pp.299~318 (1992)
- "2021 Written Verdict"[URL]
- MATLAB, "MATLAB, Statistical Toolbox Release 2018b", The MathWorks, Inc., ~ (2018)
- Montewka, J., "Marine traffic risk modelling–an innovative approach and a case study", Journal of Risk and Reliability, 225(3), pp.307~322 (2011)
- 박득진, "선원 행동오류에 대한 최적 확률분포함수 추정에 관한 연구", 한국항해항만학회지, 43(1), pp.1~8 (2019), 한국항해항만학회[DOI]
- Qiao, W., "Cognitive Gap and Correlation of Safety-I and Safety-II: A Case of Maritime Shipping Safety Management", Sustainability, 13(10), pp.5509~ (2021)
- Rothblum, A. M., "Human error and marine safety", National Safety Council Congress and Expo (2000)
- Senders, John, W., "Human error: cause, prediction, and reduction", pp.25~ (1991), Lawrence Erlbaum Associates
- Smith, S. P., "Improving hazard classification through the reuse of descriptive arguments", International Conference on Software Reuse (2002), Springer
- Wasserman, L., "All of statistics: a concise course in statistical inference" (2013), Springer Science & Business Media
- Yildiz, S., "Application of the HFACS-PV approach for identification of human and organizational factors (HOFs) influencing marine accidents", Reliability Engineering & System Safety, 208, pp.107395~ (2021)
- 임정빈, "상선 운항 사고의 양적 위기평가기법 개발", 한국항해항만학회지, 33(1), pp.9~19 (2009), 한국항해항만학회
- 임정빈, "해양사고 원인을 분류하기 위한 공통단어의 축소에 관한 연구", 한국항해항만학회지, 41(3), pp.109~118 (2017), 한국항해항만학회[DOI]