Data-driven and hybrid coastal morphological prediction methods for mesoscale forecasting
- Submitting institution
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Cardiff University / Prifysgol Caerdydd
- Unit of assessment
- 12 - Engineering
- Output identifier
- 97082649
- Type
- D - Journal article
- DOI
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10.1016/j.geomorph.2015.10.016
- Title of journal
- Geomorphology
- Article number
- -
- First page
- 49
- Volume
- 256
- Issue
- -
- ISSN
- 0169-555X
- Open access status
- Out of scope for open access requirements
- Month of publication
- November
- Year of publication
- 2015
- URL
-
http://dx.doi.org/10.1016/j.geomorph.2015.10.016
- Supplementary information
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-
- 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
-
5
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This research is the outcome of the NERC funded project iCOASST project (NE/J005606/1) and provides a novel approach to improving the understanding of the meso-scale coastal morphological evolution along the east coast of the UK, and also offers practical tools for long-term (decadal) beach and shoreline evolution. The Environment Agency provided additional funds for a manual based on the iCOASST methods and the wider issue of predicting decadal and centennial geomorphic evolution with a demonstration case study. It has also been referenced in UNFCCC Guidance on coastal impact assessment for climate change (http://gtr.ukri.org/projects?ref=NE/J005541/1).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -