Matches in SemOpenAlex for { <https://semopenalex.org/work/W4312124128> ?p ?o ?g. }
- W4312124128 endingPage "1748006X2211399" @default.
- W4312124128 startingPage "1748006X2211399" @default.
- W4312124128 abstract "In recent decades, many attempts have been made to establish the cause-effect relationship model of accidents, while little work has been carried out to comprehensively consider the interdependence between the causal factors and their complex interactions with the accident outcomes. In this study, a novel accident analysis approach based on Bayesian networks (BNs) was proposed to achieve quantitative accident analysis and dynamic risk prediction of accident types and consequences. To develop the BN-based accident analysis model, a total of 1144 accident cases occurred in tank farm of China from 1960 to 2018 were collected. The BN model that can comprehensively characterize the dependencies among accident elements was established through structural learning based on accident case analysis and parameter learning based on EM algorithm. The reliability and validity of the BN model were verified by k-fold cross-validation method and comparison of predicted data with real data, and the results showed that the BN model had good classification and prediction performance. Furthermore, the established BN model was applied to the accident occurred in Huangdao, China. The analysis results show that not only the accident outcome can be accurately predicted, but also the hidden correlation can be deeply explored through the established BN model. The proposed method and findings can provide technical reference for accident investigation and analysis, and provide decision support for accident prevention and risk management." @default.
- W4312124128 created "2023-01-04" @default.
- W4312124128 creator A5007601689 @default.
- W4312124128 creator A5019182546 @default.
- W4312124128 creator A5036195075 @default.
- W4312124128 creator A5049814360 @default.
- W4312124128 creator A5073774647 @default.
- W4312124128 creator A5076880929 @default.
- W4312124128 creator A5091780439 @default.
- W4312124128 date "2022-12-11" @default.
- W4312124128 modified "2023-10-07" @default.
- W4312124128 title "Accident analysis and risk prediction of tank farm based on Bayesian network method" @default.
- W4312124128 cites W1535430927 @default.
- W4312124128 cites W1984045663 @default.
- W4312124128 cites W1995504003 @default.
- W4312124128 cites W2003314328 @default.
- W4312124128 cites W2012075987 @default.
- W4312124128 cites W2014718531 @default.
- W4312124128 cites W2032363209 @default.
- W4312124128 cites W2033448819 @default.
- W4312124128 cites W2039288204 @default.
- W4312124128 cites W2045759663 @default.
- W4312124128 cites W2046328132 @default.
- W4312124128 cites W2049633694 @default.
- W4312124128 cites W2054309874 @default.
- W4312124128 cites W2056071679 @default.
- W4312124128 cites W2060027500 @default.
- W4312124128 cites W2063264027 @default.
- W4312124128 cites W2064849006 @default.
- W4312124128 cites W2068183470 @default.
- W4312124128 cites W2073777654 @default.
- W4312124128 cites W2083877768 @default.
- W4312124128 cites W2084746994 @default.
- W4312124128 cites W2086927126 @default.
- W4312124128 cites W2087244322 @default.
- W4312124128 cites W2093421556 @default.
- W4312124128 cites W2100372437 @default.
- W4312124128 cites W2104623801 @default.
- W4312124128 cites W2132434674 @default.
- W4312124128 cites W2149257935 @default.
- W4312124128 cites W2190031236 @default.
- W4312124128 cites W2198346408 @default.
- W4312124128 cites W2214001964 @default.
- W4312124128 cites W2395107604 @default.
- W4312124128 cites W2425959492 @default.
- W4312124128 cites W2505556904 @default.
- W4312124128 cites W2552220595 @default.
- W4312124128 cites W2753048242 @default.
- W4312124128 cites W2758612542 @default.
- W4312124128 cites W2782924405 @default.
- W4312124128 cites W2792505167 @default.
- W4312124128 cites W2884762388 @default.
- W4312124128 cites W2904128238 @default.
- W4312124128 cites W2907872577 @default.
- W4312124128 cites W2911890521 @default.
- W4312124128 cites W2912584734 @default.
- W4312124128 cites W2955673048 @default.
- W4312124128 cites W2965640442 @default.
- W4312124128 cites W2972794580 @default.
- W4312124128 cites W2974873344 @default.
- W4312124128 cites W2989706182 @default.
- W4312124128 cites W3034750607 @default.
- W4312124128 cites W3137260098 @default.
- W4312124128 cites W3194347863 @default.
- W4312124128 cites W4210845851 @default.
- W4312124128 cites W4237482698 @default.
- W4312124128 cites W95496284 @default.
- W4312124128 doi "https://doi.org/10.1177/1748006x221139906" @default.
- W4312124128 hasPublicationYear "2022" @default.
- W4312124128 type Work @default.
- W4312124128 citedByCount "0" @default.
- W4312124128 crossrefType "journal-article" @default.
- W4312124128 hasAuthorship W4312124128A5007601689 @default.
- W4312124128 hasAuthorship W4312124128A5019182546 @default.
- W4312124128 hasAuthorship W4312124128A5036195075 @default.
- W4312124128 hasAuthorship W4312124128A5049814360 @default.
- W4312124128 hasAuthorship W4312124128A5073774647 @default.
- W4312124128 hasAuthorship W4312124128A5076880929 @default.
- W4312124128 hasAuthorship W4312124128A5091780439 @default.
- W4312124128 hasConcept C107673813 @default.
- W4312124128 hasConcept C111472728 @default.
- W4312124128 hasConcept C119857082 @default.
- W4312124128 hasConcept C121332964 @default.
- W4312124128 hasConcept C124101348 @default.
- W4312124128 hasConcept C127413603 @default.
- W4312124128 hasConcept C138885662 @default.
- W4312124128 hasConcept C154945302 @default.
- W4312124128 hasConcept C163258240 @default.
- W4312124128 hasConcept C200601418 @default.
- W4312124128 hasConcept C2780289543 @default.
- W4312124128 hasConcept C2780591428 @default.
- W4312124128 hasConcept C33724603 @default.
- W4312124128 hasConcept C41008148 @default.
- W4312124128 hasConcept C43214815 @default.
- W4312124128 hasConcept C62520636 @default.
- W4312124128 hasConceptScore W4312124128C107673813 @default.
- W4312124128 hasConceptScore W4312124128C111472728 @default.
- W4312124128 hasConceptScore W4312124128C119857082 @default.