scholarly journals Evaluation of a Bayesian Network for Strengthening the Weight of Evidence to Predict Acute Fish Toxicity from Fish Embryo Toxicity Data

2020 ◽  
Vol 16 (4) ◽  
pp. 452-460 ◽  
Author(s):  
Adam Lillicrap ◽  
S Jannicke Moe ◽  
Raoul Wolf ◽  
Kristin A Connors ◽  
Jane M Rawlings ◽  
...  
Keyword(s):  
Bayesian Network ◽  
Weight Of Evidence ◽  
Toxicity Data ◽  
Fish Embryo ◽  
Fish Toxicity ◽  
Embryo Toxicity

scholarly journals Development of a hybrid Bayesian network model for predicting acute fish toxicity using multiple lines of evidence

2019 ◽  
Author(s):  
S. Jannicke Moe ◽  
Anders L. Madsen ◽  
Kristin A. Connors ◽  
Jane M. Rawlings ◽  
Scott E. Belanger ◽  
...  
Keyword(s):  
Bayesian Network ◽  
Expert Knowledge ◽  
Weight Of Evidence ◽  
List Type ◽  
Toxicity Data ◽  
Web Interface ◽  
Fish Embryo ◽  
Fish Toxicity ◽  
Embryo Toxicity ◽  
Lines Of Evidence

AbstractA Bayesian network was developed for predicting the acute toxicity intervals of chemical substances to fish, based on information on fish embryo toxicity (FET) in combination with other information. This model can support the use of FET data in a Weight-of-Evidence (WOE) approach for replacing the use of juvenile fish. The BN predicted correct toxicity intervals for 69%-80% of the tested substances. The model was most sensitive to components quantified by toxicity data, and least sensitive to components quantified by expert knowledge. The model is publicly available through a web interface. Further development of this model should include additional lines of evidence, refinement of the discretisation, and training with a larger dataset for weighting of the lines of evidence. A refined version of this model can be a useful tool for predicting acute fish toxicity, and a contribution to more quantitative WOE approaches for ecotoxicology and environmental assessment more generally.HighlightsA Bayesian network (BN) was developed to predict the toxicity of chemicals to fishThe BN uses fish embryo toxicity data in a quantitative weight-of-evidence approachThe BN integrates physical, chemical and toxicological properties of chemicalsCorrect toxicity intervals were predicted for 69-80% of test casesThe BN is publicly available for demonstration and testing through a web interface

Use of fish embryo toxicity tests for the prediction of acute fish toxicity to chemicals

2013 ◽  
Vol 32 (8) ◽  
pp. 1768-1783 ◽  
Author(s):  
Scott E. Belanger ◽  
Jane M. Rawlings ◽  
Gregory J. Carr
Keyword(s):  
Toxicity Tests ◽  
Fish Embryo ◽  
Fish Toxicity ◽  
Embryo Toxicity

Fish embryo tests and acute fish toxicity tests are interchangeable in the application of the threshold approach

2019 ◽  
Vol 38 (3) ◽  
pp. 671-681 ◽  
Author(s):  
Jane M. Rawlings ◽  
Scott E. Belanger ◽  
Kristin A. Connors ◽  
Gregory J. Carr
Keyword(s):  
Toxicity Tests ◽  
Fish Embryo ◽  
Fish Toxicity ◽  
Threshold Approach

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

2021 ◽  
Vol 10 (2) ◽  
pp. 330-347
Author(s):  
Ana Kuzmanić Skelin ◽  
Lea Vojković ◽  
Dani Mohović ◽  
Damir Zec
Keyword(s):  
Risk Assessment ◽  
Bayesian Network ◽  
Bayes Factor ◽  
Quantitative Estimation ◽  
Influencing Factor ◽  
Probabilistic Approach ◽  
Weight Of Evidence ◽  
Accident Risk ◽  
Strength Of Evidence ◽  
Bayesian Network Classifier

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 risk 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.

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