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Sains
Malaysiana 51(7)(2022):
http://doi.org/10.17576/jsm-2022-5107-29
Pengukuran Risiko menggunakan
Rangkaian Bayesan: Aplikasi kepada Data Perlanggaran Kapal di Malaysia
(Risk Measurement using Bayesian Networks: Applications to
Ship Collision Data in Malaysia)
ZAMIRA
HASANAH ZAMZURI1,2,* & ZAIDI ISA1,2
1Jabatan
Sains Matematik, Fakulti Sains dan Teknologi, Universiti Kebangsaan Malaysia,
43600 UKM Bangi, Selangor Darul Ehsan, Malaysia
2Pusat Pemodelan dan Analisis Data, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor Darul Ehsan, Malaysia
Received: 13 March 2022/Accepted: 14 April 2022
Abstrak
Mengenal
pasti faktor berbahaya yang mengancam keselamatan adalah penting agar tindakan
dapat dirancang untuk menangani akibat jika berlakunya hazard tersebut. Proses
pengecaman tersebut memerlukan pengukuran dan komputasi khusus dengan
kebarangkalian dan keparahan kejadian tersebut diperlukan. Dalam kajian ini,
pendekatan rangkaian Bayesan dilaksanakan untuk menilai, mengenal pasti dan kemudiannya
memberi pangkat kepada faktor yang menyumbang kepada berlakunya perlanggaran
kapal. Menerusi penggabungan maklumat daripada pandangan pakar dan data lepas,
satu rangkaian Bayesan dibina untuk mentafsir kebarangkalian berlakunya
perlanggaran diberi pemboleh ubah yang dicerap. Tiga jenis hazard
dipertimbangkan dalam kajian ini iaitu teknikal, semula jadi dan kesilapan
manusia. Peningkatan dalam kebarangkalian berlakunya perlanggaran kemudiannya
dihitung dengan mensyaratkan kepada aras dalam pemboleh ubah dicerap. Tiga
faktor pertama yang menyumbang kepada berlakunya perlanggaran kapal adalah
kegagalan major dalam sistem komunikasi, kebolehan terjejas tinggi dan
ketiadaan pemandu sebagai penasihat pelayaran. Penemuan ini membantu dalam
menyerlahkan potensi major ditawarkan oleh rangkaian Bayesan bagi analisis
risiko dan penghitungan kebarangkalian. Malahan, kajian ini menawarkan
pemahaman yang lebih mendalam kepada pengamal dalam bidang ini untuk merancang
atau membina strategi tindakan yang diperlukan bagi mengelakkan berlakunya
perlanggaran. Maklumat ini juga penting untuk menilai keselamatan sesuatu kapal
dalam usaha mengurangkan potensi suatu perlanggaran tersebut berlaku.
Kata kunci: Bayesan;
perlanggaran kapal; rangkaian; risiko
Abstract
Identifying hazardous factors
that threaten safety is important so that actions can be planned to address the
consequences of the hazard. The identification process requires specific
measurements and computations, for which the probability and severity of the
event are required. In this study, the Bayesan network approach is implemented
to evaluate, identify and then rank the factors that contribute to the
occurrence of ship collisions. Through a combination of information from expert
views and past data, a Bayesian network was constructed to interpret the
probability of a collision given the observed variables. Three types of hazards
are considered in this study namely technical, natural, and human error. The
increase in the probability of a collision is then calculated by conditional to
the level in the observed variable. The first three factors that contribute to
the occurrence of shipwrecks are major failures in communication systems, high
impaired abilities and the absence of a driver as a navigational advisor. These
findings help in highlighting the major potentials offered by the Bayesian network for risk analysis and probability calculations. In fact, this study
offers a deeper understanding to practitioners in this field to plan or build
the necessary action strategies to avoid the occurrence of collisions. This
information is also important in assessing the safety of a ship in an effort to
reduce the potential for a collision to occur.
Keywords: Bayesian; network; risk;
ship collision
REFERENCES
Badida, P., Balasubramaniam, Y. & Jayaprakash, J.
2019. Risk evaluation of oil and natural gas pipelines due to natural hazards
using fuzzy fault tree analysis. J. Nat.
Gas Sci. Eng. 66: 284-292.
Chai, T., Weng, J. & Xiong, D. 2017. Development
of a quantitative risk assessment model for ship collisions in fairways. Safety Science 91: 71-83.
Champ, P.A. & Brenkert‐Smith, H. 2016. Is
seeing believing? Perceptions of wildfire risk over time. Risk Analysis 36(4): 816-830.
Chen, S., Chai, L., Xu, K., Wei, Y., Rong, Z. &
Wan, W. 2019. Estimation of the occurrence probability of extreme geomagnetic
storms by applying extreme value theory to Aa index. JGR Space Physics 124: 9943-9952.
Chen, P., Huang, Y., Mou, J. & van Gelder,
P.H.A.J.M. 2019. Probabilistic risk analysis for ship-ship collision:
State-of-the-art. Safety Science 117:
108-122.
Constantinou, A.C., Fenton, N.E. & Neil, M. 2016.
Integrating expert knowledge with data in Bayesian networks: Preserving
data-driven expectations when the expert variables remain unobserved. Expert System with Applications 56:
197-208.
Dharmarathne, G., Hanea, A. & Robinson, A.P. 2021.
Improving the computation of Brier scores for evaluating expert-elicited
judgement. Frontiers in Applied
Mathematics and Statistics. https://doi.org/10.3389/fams.2021.669546
Fowler, T.G. & Sørgård, E. 2000. Modeling ship
transportation risk. Risk Analysis 20(2): 225-244.
Friedman, N. & Koller, D. 2003. Being Bayesian
about network structure. A Bayesian approach to structure discovery in Bayesian
networks. Machine Learning 50:
95-125.
Gusmao, A.P.H., Silva, M.M., Poleto, T., Silve, L.C.
& Costa, A.P.C.S. 2018. Cybersecurity risk analysis model using fault tree
analysis and fuzzy decision theory. International
Journal of Information Management 43: 248-260.
Hänninen, M. & Kujala, K. 2012. Influences of
variables on ship collision probability in a Bayesian belief network model. Reliability Engineering 102: 27-40.
Haron, S.J. 2015. Analysis of marine incidents in
Malaysia. Master Thesis, Universiti Teknologi Malaysia (UTM) (Tidak
diterbitkan).
Karahalios, H. 2014. The contribution of risk
management in ship management: The case of ship collision. Safety Science 63: 104-114. https://doi.org/10.1016/j.ssci.2013.11.004.
Khakzad, N., Khan, F. & Amyotte, P. 2011. Safety
analysis in process facilities: Comparison of fault tree and Bayesian network approaches. Reliability Engineering & System
Safety 96(8): 925-932.
Korb, K. & Nicholson, A. 2003. Bayesian Artificial Intelligence. Boca
Raton: CRC Press.
Luxhøj, J.T. 2015. A conceptual Object-Oriented
Bayesian Network (OOBN) for modeling aircraft carrier-based UAS safety risk. Journal of Risk Research 18(10):
1230-1258.
Macduff, T. 1974. The probability of vessel
collisions. Ocean Industry 9(9):
144-148.
Makajic-Nikolica, D., Petrovica, N., Cirovica, M.,
Vujosevica, M. & Presburger-Ulnikovic, V. 2016. The model of risk
assessment of greywater discharges from the Danube River ships. Journal of Risk Research 19(4): 496-514.
Marcot, B.G. 2012. Metrics for evaluating performance
and uncertainty of Bayesian network models. Ecological
Modelling 230: 50-62.
Martins, M.R. & Maturana, M.C. 2009. The
application of the Bayesian networks in the human reliability analysis. Proceedings of the ASME 2009 International
Mechanical Engineering Congress and Exposition, Lake Buena Vista, FL, 13-19
November. New York: IEEE.
Neapolitan, R.E. & Jiang, X. 2007. Probabilistic Methods for Financial and
Marketing Informatics. San Francisco: Morgan Kaufmann Publishers.
Otto, S., Pedersen, P.T., Samuelides, M. & Sames,
P.C. 2002. Elements of risk analysis for collision and grounding of a RoRo
passenger ferry. Marine Structures 15(4): 461-474.
Razak, R.A. & Ismail, N. 2019. Dependence modeling
and portfolio risk estimation using GARCH-Copula approach. Sains Malaysiana 48(7): 1547-1555.
Todinov, M. 2019. Mechanisms for improving reliability
and reducing risk by stochastic and deterministic separation. Journal of Risk Research 22(4): 448-474.
Weber, P., Medina-Oliva, G., Simon, C. & Iung, B.
2012. Overview on Bayesian networks applications for dependability, risk
analysis and maintenance areas. Engineering
Applications of Artificial Intelligence 25(4): 671-682.
Xin, Z. & Wen, L. 2017. A Bayesian network
approach to causation analysis of road accidents using Netica. Journal of Advanced Transportationhttps://doi.org/10.1155/2017/2525481
Yazdi, M., Nikfar, F. & Nasrabadi, M. 2017.
Failure probability analysis by employing fuzzy fault tree analysis. International Journal of System Assurance
Engineering and Management 8(2): 1177-1193.
Zaman, M.B., Kobayashi, E., Wakabayashi, N., Khanfir,
S., Pitana, T. & Maimun, A. 2014. Fuzzy FMEA model for risk evaluation of
ship collisions in the Malacca Strait: Based on AIS data. Journal of Simulation 8(1): 91-104.
Zamzuri, Z. & Gwee, J.H. 2020. Comparing and
forecasting using stochastic mortality models: A Monte Carlo simulation. Sains Malaysiana 49(8): 2013-2022.
Zhang, J., Teixiera, A.P., Guandes Soares, C., Yan, X.
& Liu, K. 2016. Maritime transportation risk assessment of Tianjin Port
using Bayesian belief network. Risk
Analysis 36(6): 1171-1187.
Zhang, J. 2017. Life-Oriented
Behavioral Research for Urban Policy. Japan: Springer.
*Corresponding author; email: zamira@ukm.edu.my
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