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Fuzzy Bayesian Modeling of Sea-Level Along the East Coast of Britain

J Lawry, K Horsburgh, D Cluckie, I, Ian Cluckie

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...

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Published in: IEEE Transactions on Fuzzy Systems
ISSN: 1063-6706
Published: 2008
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa10541
first_indexed 2013-07-23T12:03:47Z
last_indexed 2018-02-09T04:39:24Z
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spelling 2013-11-18T17:30:11.2219672 v2 10541 2012-04-06 Fuzzy Bayesian Modeling of Sea-Level Along the East Coast of Britain d801af52a3cfb625308bd4301583064e Ian Cluckie Ian Cluckie true false 2012-04-06 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. Journal Article IEEE Transactions on Fuzzy Systems 16 3 725 738 1063-6706 Fuzzy, Naive-Bayes, Harmonic Tidal Prediction, Sea Level, Surge, Tide 30 6 2008 2008-06-30 10.1109/TFUZZ.2008.919278 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. COLLEGE NANME COLLEGE CODE Swansea University 2013-11-18T17:30:11.2219672 2012-04-06T19:47:12.3792466 Faculty of Science and Engineering School of Engineering and Applied Sciences - Uncategorised J Lawry 1 K Horsburgh 2 D Cluckie, I 3 Ian Cluckie 4
title Fuzzy Bayesian Modeling of Sea-Level Along the East Coast of Britain
spellingShingle Fuzzy Bayesian Modeling of Sea-Level Along the East Coast of Britain
Ian Cluckie
title_short Fuzzy Bayesian Modeling of Sea-Level Along the East Coast of Britain
title_full Fuzzy Bayesian Modeling of Sea-Level Along the East Coast of Britain
title_fullStr Fuzzy Bayesian Modeling of Sea-Level Along the East Coast of Britain
title_full_unstemmed Fuzzy Bayesian Modeling of Sea-Level Along the East Coast of Britain
title_sort Fuzzy Bayesian Modeling of Sea-Level Along the East Coast of Britain
author_id_str_mv d801af52a3cfb625308bd4301583064e
author_id_fullname_str_mv d801af52a3cfb625308bd4301583064e_***_Ian Cluckie
author Ian Cluckie
author2 J Lawry
K Horsburgh
D Cluckie, I
Ian Cluckie
format Journal article
container_title IEEE Transactions on Fuzzy Systems
container_volume 16
container_issue 3
container_start_page 725
publishDate 2008
institution Swansea University
issn 1063-6706
doi_str_mv 10.1109/TFUZZ.2008.919278
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Uncategorised{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Uncategorised
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description 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.
published_date 2008-06-30T04:36:39Z
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score 11.096913

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