Name
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. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/TFUZZ.2008.919278 | IEEE T. Fuzzy Systems |
Keywords | Field | DocType |
east coast,thames estuary,fuzzy bayesian modeling,fuzzy bayesian algorithm,low-dimensional model,tidal gauge measurement,astronomical tidal prediction,fast execution time,meteorological data,low computational cost,weather forecasting,predictive models,meteorology,tides,marine sciences,artificial intelligence,sea level,bayesian model,surge,tide,fuzzy set theory,bayesian methods,environmental management | Meteorology,Bayesian algorithm,Bayesian inference,Sea level,Fuzzy logic,Fuzzy set,Estuary,Surge,Artificial intelligence,Sextant (astronomical),Mathematics,Machine learning | Journal |
Volume | Issue | ISSN |
16 | 3 | 1063-6706 |
Citations | PageRank | References |
4 | 0.51 | 6 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| N. J. Randon | 1 | 13 | 1.22 |
| Jonathan Lawry | 2 | 172 | 19.06 |
| K. Horsburgh | 3 | 4 | 0.51 |
| I. D. Cluckie | 4 | 9 | 1.24 |