A factorial Bayesian copula framework for partitioning uncertainties in multivariate risk inference
- Submitting institution
-
Brunel University London
- Unit of assessment
- 12 - Engineering
- Output identifier
- 361-216062-7001443
- Type
- D - Journal article
- DOI
-
10.1016/j.envres.2020.109215
- Title of journal
- Environmental Research
- Article number
- 109215
- First page
- -
- Volume
- 183
- Issue
- 2020-04-01
- ISSN
- 0013-9351
- Open access status
- Compliant
- Month of publication
- February
- Year of publication
- 2020
- URL
-
http://bura.brunel.ac.uk/handle/2438/20388
- Supplementary information
-
https://ars.els-cdn.com/content/image/1-s2.0-S0013935120301079-mmc1.docx
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
3
- Research group(s)
-
1 - Energy & Environment
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Uncertainties are extensively embodied in hydrologic risk analysis. Particularly for interdependent hydrometeorological extremes, the random features in individual variables and their dependence structures may lead to bias and uncertainty in future risk inferences. Based on extensive collaboration among UK, China and Canada, this paper is a first attempt to propose a factorial Bayesian copula (FBC) approach to i) quantify parameter uncertainties in multivariate risk inference models and ii) track the major contributors for the uncertainties in risk inferences. The proposed FBC approach has extensive application potentials for risk assessment issues such as compound hydroclimatic extremes, water quality, and air pollution
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -