Abstract
Tide tables are the method of choice for water level predictions in most coastal regions. In the United States, the National Ocean Service (NOS) uses harmonic analysis and time series of previous water levels to compute tide tables. This method is adequate for most locations along the US coast. However, for many locations along the coast of the Gulf of Mexico, tide tables do not meet NOS criteria. Wind forcing has been recognized as the main variable not included in harmonic analysis. The performance of the tide charts is particularly poor in shallow embayments along the coast of Texas. Recent research at Texas A&M University-Corpus Christi has shown that Artificial Neural Network (ANN) models including input variables such as previous water levels, tidal forecasts, wind speed, wind direction, wind forecasts and barometric pressure can greatly improve water level predictions at several coastal locations including open coast and deep embayment stations. In this paper, the ANN modeling technique was applied for the first time to a shallow embayment, the station of Rockport located near Corpus Christi, Texas. The ANN performance was compared to the NOS tide charts and the persistence model for the years 1997 to 2001. This site was ideal because it is located in a shallow embayment along the Texas coast and there is an 11-year historical record of water levels and meteorological data in the Texas Coastal Ocean Observation Network (TCOON) database. The performance of the ANN model was measured using NOS criteria such as Central Frequency (CF), Maximum Duration of Positive Outliers (MDPO), and Maximum Duration of Negative Outliers (MDNO). The ANN model compared favorably to existing models using these criteria and is the best predictor of future water levels tested.
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© 2005 Springer-Verlag Berlin Heidelberg
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Steidley, C., Sadovski, A., Tissot, P., Bachnak, R., Bowles, Z. (2005). Using an Artificial Neural Network to Improve Predictions of Water Levels Where Tide Charts Fail. In: Ali, M., Esposito, F. (eds) Innovations in Applied Artificial Intelligence. IEA/AIE 2005. Lecture Notes in Computer Science(), vol 3533. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11504894_83
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DOI: https://doi.org/10.1007/11504894_83
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