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Pregled bibliografske jedinice broj: 1159389

Weight of Evidence Approach to Maritime Accident Risk Assessment Based on Bayesian Network Classifier


Kuzmanić Skelin, Ana; Vojković, Lea; Mohović, Đani; Zec, Damir;
Weight of Evidence Approach to Maritime Accident Risk Assessment Based on Bayesian Network Classifier // Transactions on maritime science, 10 (2021), 2; 330-347 doi:10.7225/toms.v10.n02.w07 (međunarodna recenzija, članak, znanstveni)


CROSBI ID: 1159389 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Weight of Evidence Approach to Maritime Accident Risk Assessment Based on Bayesian Network Classifier

Autori
Kuzmanić Skelin, Ana ; Vojković, Lea ; Mohović, Đani ; Zec, Damir ;

Izvornik
Transactions on maritime science (1848-3305) 10 (2021), 2; 330-347

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Maritime collision model ; Probabilistic modelling ; Bayesian Network classifier ; Weight of evidence ; Bayes factor ; Probabilistic reasoning

Sažetak
Probabilistic maritime accident models based on Bayesian Networks are typically built upon the data available in accident records and the data obtained from human experts’ knowledge on accident. The drawback of such models is that they do not take explicitly into the account the knowledge on non-accidents as would be required in the probabilistic modelling of rare events. Consequently, these models have difficulties with delivering interpretation of influence of risk factors and providing sufficient confidence in the risk assessment scores. In this work, modelling and risk score interpretation, as two aspects of the probabilistic approach to complex maritime system risk assessment, are addressed. First, the maritime accident modelling is posed as a classification problem and the Bayesian network classifier that discriminates between accident and non-accident is developed which assesses state spaces of influence factors as the input features of the classifier. Maritime accident risks are identified as adversely influencing factors that contribute to the accident. Next, the weight of evidence approach to reasoning with Bayesian network classifier is developed for an objective quantitative estimation of the strength of factor influence, and a weighted strength of evidence is introduced. Qualitative interpretation of strength of evidence for individual accident influencing factor, inspired by Bayes factor, is defined. The efficiency of the developed approach is demonstrated within the context of collision of small passenger vessels and the results of collision risk assessments are given for the environmental settings typical in Croatian nautical tourism. According to the results obtained, recommendations for navigation safety during high density traffic have been distilled.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo, Tehnologija prometa i transport, Informacijske i komunikacijske znanosti



POVEZANOST RADA


Ustanove:
Fakultet elektrotehnike, strojarstva i brodogradnje, Split,
Pomorski fakultet, Rijeka,
Pomorski fakultet, Split

Profili:

Avatar Url Đani Mohović (autor)

Avatar Url Ana Kuzmanić Skelin (autor)

Avatar Url Damir Zec (autor)

Avatar Url Lea Vojković (autor)

Poveznice na cjeloviti tekst rada:

doi hrcak.srce.hr

Citiraj ovu publikaciju:

Kuzmanić Skelin, Ana; Vojković, Lea; Mohović, Đani; Zec, Damir;
Weight of Evidence Approach to Maritime Accident Risk Assessment Based on Bayesian Network Classifier // Transactions on maritime science, 10 (2021), 2; 330-347 doi:10.7225/toms.v10.n02.w07 (međunarodna recenzija, članak, znanstveni)
Kuzmanić Skelin, A., Vojković, L., Mohović, Đ., Zec, D. & (2021) Weight of Evidence Approach to Maritime Accident Risk Assessment Based on Bayesian Network Classifier. Transactions on maritime science, 10 (2), 330-347 doi:10.7225/toms.v10.n02.w07.
@article{article, author = {Kuzmani\'{c} Skelin, Ana and Vojkovi\'{c}, Lea and Mohovi\'{c}, \DJani and Zec, Damir}, year = {2021}, pages = {330-347}, DOI = {10.7225/toms.v10.n02.w07}, keywords = {Maritime collision model, Probabilistic modelling, Bayesian Network classifier, Weight of evidence, Bayes factor, Probabilistic reasoning}, journal = {Transactions on maritime science}, doi = {10.7225/toms.v10.n02.w07}, volume = {10}, number = {2}, issn = {1848-3305}, title = {Weight of Evidence Approach to Maritime Accident Risk Assessment Based on Bayesian Network Classifier}, keyword = {Maritime collision model, Probabilistic modelling, Bayesian Network classifier, Weight of evidence, Bayes factor, Probabilistic reasoning} }
@article{article, author = {Kuzmani\'{c} Skelin, Ana and Vojkovi\'{c}, Lea and Mohovi\'{c}, \DJani and Zec, Damir}, year = {2021}, pages = {330-347}, DOI = {10.7225/toms.v10.n02.w07}, keywords = {Maritime collision model, Probabilistic modelling, Bayesian Network classifier, Weight of evidence, Bayes factor, Probabilistic reasoning}, journal = {Transactions on maritime science}, doi = {10.7225/toms.v10.n02.w07}, volume = {10}, number = {2}, issn = {1848-3305}, title = {Weight of Evidence Approach to Maritime Accident Risk Assessment Based on Bayesian Network Classifier}, keyword = {Maritime collision model, Probabilistic modelling, Bayesian Network classifier, Weight of evidence, Bayes factor, Probabilistic reasoning} }

Časopis indeksira:


  • Web of Science Core Collection (WoSCC)
    • Emerging Sources Citation Index (ESCI)
  • Scopus


Citati:





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