Bayesian Data-Driven approach enhances synthetic flood loss models
- Submitting institution
-
Middlesex University
- Unit of assessment
- 14 - Geography and Environmental Studies
- Output identifier
- 1699
- Type
- D - Journal article
- DOI
-
10.1016/j.envsoft.2020.104798
- Title of journal
- Environmental Modelling and Software
- Article number
- 104798
- First page
- -
- Volume
- 132
- Issue
- -
- ISSN
- 1364-8152
- Open access status
- Compliant
- Month of publication
- July
- Year of publication
- 2020
- URL
-
http://eprints.mdx.ac.uk/30806/
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
-
10
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- -
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -