Chapter
Apr 26, 2012
Neural Network Forecasting of Storm Surges along the Gulf of Mexico
Authors: Philippe E. Tissot [email protected], Daniel T. Cox [email protected], and Patrick Michaud [email protected]Author Affiliations
Publication: Ocean Wave Measurement and Analysis (2001)
Abstract
Accurate water level forecasts are of vital importance along the Gulf of Mexico as its waterways play a critical economie role for a number of industries including shipping, oil and gas, tourism, and fisheries. While astronomical forcing (tides) is well tabulated, water level changes along the Gulf Coast are frequently dominated by meteorological factors. Their impact is often larger than the tidal range itself and unaccounted for in present forecasts. We have taken advantage of the increasing availability of real time data for the Texas Gulf Coast and have developed neural network models to forecast future water levels. The selected inputs to the model include water levels, wind stress, barometric pressures as well as tidal forecasts and wind forecasts. A very simple neural network structure is found to be optimal for this problem. The performance of the model is computed for forecasting times between l and 48 hours and compared with the tide tables. The model is alternatively trained and tested using three-month data sets from the 1997, 1998 and 1999 records of the Pleasure Pier Station located on Galveston Island near Houston, Texas. Models including wind forecasts outperform other models and are considerably more accurate than the tide tables for the forecasting time range tested, demonstrating the viability of neural network based models for the forecasting of water levels along the Gulf Coast.
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© 2002 American Society of Civil Engineers.
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Published online: Apr 26, 2012
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Assistant Professor, Physics, Physical and Life Sciences Department, and Research Faculty, Conrad Blucher Institute, Texas A&M University-Corpus Christi, Corpus Christi, Texas 78411. E-mail: [email protected]
M.ASCE
Associate Professor, Coastal and Ocean Engineering Division, Civil Engineering Department, Texas A&M University, College Station, Texas 77843-3136. E-mail: [email protected]
Professor of Computer Sciences, Computing and Mathematical Sciences Department, and Director, Division of Near Shore Research, Conrad Blucher Institute, Texas A&M University-Corpus Christi, Corpus Christi, Texas 78411. E-mail: [email protected]
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