Ocean Wave Measurement and Analysis (2001) 2002
DOI: 10.1061/40604(273)155
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Neural Network Forecasting of Storm Surges along the Gulf of Mexico

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Cited by 15 publications
(9 citation statements)
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“…Hsieh (2009) describes ways to use nonlinear principal component analysis, based on NNs, to better analyze tidal data. Tissot et al (2002) and Cox et al (2002) report integrating NN and statistical approaches to predict water levels in the microtidal shallow waters of the Gulf of Mexico where atmospheric forcings often dominate. Collins and Tissot (2015) used an NN to predict thunderstorms in southern Texas.…”
Section: ) Weather Forecastingmentioning
confidence: 99%
“…Hsieh (2009) describes ways to use nonlinear principal component analysis, based on NNs, to better analyze tidal data. Tissot et al (2002) and Cox et al (2002) report integrating NN and statistical approaches to predict water levels in the microtidal shallow waters of the Gulf of Mexico where atmospheric forcings often dominate. Collins and Tissot (2015) used an NN to predict thunderstorms in southern Texas.…”
Section: ) Weather Forecastingmentioning
confidence: 99%
“…On the basis of the hourly time series of water-level data, Rajasekaran, Lee, and Jeng (2005) and Lee (2006) applied functional and sequential learning neural networks in forecasting tidal levels at the same station during a weak typhoon surge in Taiwan. On the basis of selected water levels, wind stress, and barometric pressure as well as tidal forecasts and wind forecasts as inputs, Tissot, Cox, and Michaud (2001) presented a more comprehensive neural network model in forecasting hurricane storm surges along the Gulf of Mexico. Their model was alternatively trained and tested using 3-month data sets from the 1997, 1998, and 1999 records of the Pleasure Pier Station located on Galveston Island near Houston, Texas.…”
Section: Review Of Ann Applicationsmentioning
confidence: 99%
“…Our goal is to develop and compare models forecasting the difference between observed water levels and the harmonic predictions. Approaches considered include persistence model, multivariate statistical modeling, and neural networks (Sadovski et al, 2003b), (Tissot et al, 2002). The models are built and tested based on the past observations and then applied to predict future water level differences.…”
Section: Improvement Of Predictionsmentioning
confidence: 99%