Validation of Human Centred Bayesian Networks - Case Study on a Cable Cut of an Export Cable of an Offshore Wind farm

Bayesian networks are a commonly used method in the risk and reliability domain to assess the likelihood of certain scenarios or the resilience of an infrastructure.This study is conducted based on a literature review of Bayesian networks in the maritime domain.Even though there are about 78 journal papers published on Bayesian networks in the maritime domain between 2018-2022, it is challenging to gather suitable data in the development process.Further, either a-priori or conditional probabilities are necessary depending on the node.Especially acquiring conditional probabilities is challenging due to that fact that for every state of the parent node(s) a conditional probability needs to be defined.In this work, we investigate validation methods for Bayesian networks.The selected methods have been chosen based on a careful review of the state of the art and encompass benchmark exercises, formal walkthroughs, qualitative feature tests and sensitivity analysis.The validation methods formal walk through and qualitative feature are then discussed at hand of a case study.For this purpose, a Bayesian network has been developed for a maritime security scenario.The results indicate that the validation process improves the design as well as the parametrization of Bayesian networks.

Authors

Publication Details

Published
2025-01-01
DOI
https://doi.org/10.3850/978-981-94-3281-3_esrel-sra-e2025-p9362-cd
Citations
1
Primary Topic
Risk and Safety Analysis
Type
article
Field-Weighted Citation Impact
1.40
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Validation of Human Centred Bayesian Networks - Case Study on a Cable Cut of an Export Cable of an Offshore Wind farm

Frank Sill Torres, Babette Tecklenburg
1 citations
Risk and Safety Analysis
1.40
article

Validation of Human Centred Bayesian Networks - Case Study on a Cable Cut of an Export Cable of an Offshore Wind farm

Frank Sill Torres, Babette Tecklenburg
article en
1 citations

Abstract

Bayesian networks are a commonly used method in the risk and reliability domain to assess the likelihood of certain scenarios or the resilience of an infrastructure.This study is conducted based on a literature review of Bayesian networks in the maritime domain.Even though there are about 78 journal papers published on Bayesian networks in the maritime domain between 2018-2022, it is challenging to gather suitable data in the development process.Further, either a-priori or conditional probabilities are necessary depending on the node.Especially acquiring conditional probabilities is challenging due to that fact that for every state of the parent node(s) a conditional probability needs to be defined.In this work, we investigate validation methods for Bayesian networks.The selected methods have been chosen based on a careful review of the state of the art and encompass benchmark exercises, formal walkthroughs, qualitative feature tests and sensitivity analysis.The validation methods formal walk through and qualitative feature are then discussed at hand of a case study.For this purpose, a Bayesian network has been developed for a maritime security scenario.The results indicate that the validation process improves the design as well as the parametrization of Bayesian networks.

Openalex Percentile: Top 12%
Risk and Safety Analysis
1.40
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Validation of Human Centred Bayesian Networks - Case Study on a Cable Cut of an Export Cable of an Offshore Wind farm — Frank Sill Torres, Babette Tecklenburg · (2025) | TGRS Research Map | TGRS