Neural Network Forecasting of Storm Surges along the Gulf of Mexico

Tissot, Philippe; Cox, Daniel T.; Michaud, Patrick

doi:10.1061/40604(273)155

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%

100 Years of Progress in Applied Meteorology. Part III: Additional Applications

Haupt

Kosović

McIntosh

et al. 2019

Meteorological Monographs

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Applied meteorology is an important and rapidly growing field. This chapter concludes the three-chapter series of this monograph describing how meteorological information can be used to serve society’s needs while at the same time advancing our understanding of the basics of the science. This chapter continues along the lines of Part II of this series by discussing ways that meteorological and climate information can help to improve the output of the agriculture and food-security sector. It also discusses how agriculture alters climate and its long-term implications. It finally pulls together several of the applications discussed by treating the food–energy–water nexus. The remaining topics of this chapter are those that are advancing rapidly with more opportunities for observation and needs for prediction. The study of space weather is advancing our understanding of how the barrage of particles from other planetary bodies in the solar system impacts Earth’s atmosphere. Our ability to predict wildland fires by coupling atmospheric and fire-behavior models is beginning to impact decision-support systems for firefighters. Last, we examine how artificial intelligence is changing the way we predict, emulate, and optimize our meteorological variables and its potential to amplify our capabilities. Many of these advances are directly due to the rapid increase in observational data and computer power. The applications reviewed in this series of chapters are not comprehensive, but they will whet the reader’s appetite for learning more about how meteorology can make a concrete impact on the world’s population by enhancing access to resources, preserving the environment, and feeding back into a better understanding how the pieces of the environmental system interact.

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Section: ) Weather Forecastingmentioning

confidence: 99%

100 Years of Progress in Applied Meteorology. Part III: Additional Applications

Haupt

Kosović

McIntosh

et al. 2019

Meteorological Monographs

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show abstract

“…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%

Neural Network and Harmonic Analysis for Recovering Missing Extreme Water-Level Data during Hurricanes in Florida

Huang

2009

Journal of Coastal Research

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“…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%

Real Time Web Availability of Statistical Models for Water Levels Along the Texas Coastline

Sadovski¹,

Steidley²,

Tissot³

et al. 2005

Proceedings of the Second International Conference on Informatics in Control, Automation and Robotics

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Water level forecasts are essential to the success of trade and industry in the Gulf of Mexico, but present forecasting methodologies do not provide accurate predictions for the Gulf Coast region. Tide charts produced by harmonic analysis are the existing standard, but these charts only show the effect of astronomical forces acting upon the water. While this proves to be an accurate predictor for most of the Atlantic and Pacific Coasts, water level changes along the Texas Coast are strongly effected by meteorological factors and thus require a modified prediction model, rather than harmonic analysis alone. A web-based tool was created that combines harmonic analysis with multivariate statistical modeling to predict water levels along the Texas Gulf Coast. The result is a substantial improvement on the current model with forecasts available via the World Wide Web.

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Neural Network Forecasting of Storm Surges along the Gulf of Mexico

Cited by 15 publications

References 0 publications

100 Years of Progress in Applied Meteorology. Part III: Additional Applications

100 Years of Progress in Applied Meteorology. Part III: Additional Applications

Neural Network and Harmonic Analysis for Recovering Missing Extreme Water-Level Data during Hurricanes in Florida

Real Time Web Availability of Statistical Models for Water Levels Along the Texas Coastline

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