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Fuzzy Bayesian Modeling of Sea-Level Along the East Coast of Britain
IEEE Transactions on Fuzzy Systems, Volume: 16, Issue: 3, Pages: 725 - 738
Swansea University Author: Ian Cluckie
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DOI (Published version): 10.1109/TFUZZ.2008.919278
Abstract
A fuzzy Bayesian algorithm is introduced, allowing for the incorporation of both uncertainty and fuzziness into data derived models. This is applied to predicting the sea-level near the Thames Estuary at Sheerness, from tidal gauge measurements down the east coast, astronomical tidal prediction, and...
| Published in: | IEEE Transactions on Fuzzy Systems |
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| ISSN: | 1063-6706 |
| Published: |
2008
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa10541 |
| Abstract: |
A fuzzy Bayesian algorithm is introduced, allowing for the incorporation of both uncertainty and fuzziness into data derived models. This is applied to predicting the sea-level near the Thames Estuary at Sheerness, from tidal gauge measurements down the east coast, astronomical tidal prediction, and meteorological data. We show that this approach can result in accurate, low-dimensional models with low computational costs and relatively fast execution times. Copyright © 2008 IEEE TFS. |
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| Item Description: |
The work was carried out in association with two major research grants. Initial support was from the EPSRC £20M+ Flood Risk Management Research Consortium (FRMRC) chaired by Cluckie. Additional funding came from the NERC FREE program. The research was focused on climate impact uncertainty using complex model cascades (i.e. Atmosphere to Hydrology to Hydraulics to Engineering Impact). This contribution was focused on the design of various proposed coastal infrastructure projects subject to severe storms. The primary thrust was the development of self-learning approaches to treating uncertainty in complex modelling systems. IF 5.48. |
| Keywords: |
Fuzzy, Naive-Bayes, Harmonic Tidal Prediction, Sea Level, Surge, Tide |
| College: |
Faculty of Science and Engineering |
| Issue: |
3 |
| Start Page: |
725 |
| End Page: |
738 |