Abstract
The paper deals with the developing of the tool to measure quality of predictions of water levels in estuaries and shallow waters of the Gulf of Mexico, when tide charts cannot provide reliable predictions. In future this goodness criteria of predictions will be applied to different regions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Cox, D.T., Tissot P.E., and Michaud P. R., Water Level Observations and Short-Term Predictions Including Meteorological Events for the Entrance of Galveston Bay, Texas, Journal of Waterways, Port, Coastal, and Ocean Engineering, 128-1, 21–29, 2002.
Thomson Bosley K, and Hess, K.W., Comparison of Statistical and Model-Based Hindcasts of Subtidal Water Levels in Chesapeake Bay, Journal of Geophysical Research, v. 106, no C8, 16,869–16,885, 2001.
Sadovski, A. L. P. Tissot, P. Michaud, C. Steidley, Statistical and Neural Network Modeling and Predictions of Tides in the Shallow Waters of the Gulf of Mexico, Proceedings of 2002 WSEAS International Conference on System Science, Applied Mathematics & Computer Science and Power Engineering Systems, Rio de Janeiro, Brazil, October, 2002.
Michaud, P., G. Jeffress, R. Dannelly, and C. Steidley 2001. Real Time Data Collection and the Texas Coastal Ocean Observation Network. Proc. International Measurement and Control (InterMAC), Tokyo, Japan, in press.
Mase, H., Sakamoto, M., and Sakai, T. 1995. Neural Network for Stability Analysis of Rubble-Mound Breakwaters. Journal of Waterway, Port, Coastal, and Ocean Engineering, 121(6), ASCE, 294–299.
Moatar F., Fessant, F., and Poirel, A. 1999. pH Modelling by Neural Networks. Application of Control and Validation Data Series in the Middle Loire River. Ecological Modeling, 120, 141–156.
Recknagel, F., French, M., Harkonen, P., and Yabunaka, K-I. 1997. Artificial Neural Network Approach for Modeling and Prediction of Algal Blooms. Ecological Modeling, 96, 11–28.
Tsai, C-P., and Lee, T-L. 1999. Back-Propagation Neural Network in Tidal-Level Forecasting. Journal of Waterway, Port, Coastal, and Ocean Engineering, 125(4), ASCE, 195–202.
Tissot P.E., Cox D.T., Michaud P. 2002. Neural Network Forecasting of Storm Surges along the Gulf of Mexico. Proceedings of the Fourth International Symposium on Ocean Wave Measurement and Analysis (Waves’ 01), ASCE, 1535–1544.
.Sadovski A.L, Multi-Objective Optimization and Decisions Based on Rating Methods of Preference Ranking, GMD FIRST, Germany, 1997.
Rumelhart, D. E., Hinton, G. E., and Williams, R.J. 1986. Learning Representations by Back-Propagating Errors. Nature, 323, 533–534.
Campolo, M., Andreussi, P., and Soldati, A. 1997. River Flood Forecasting with a Neural Network Model. Water Resources Research, 35(4), 1191–1197.
Kim, G., and Barros, A. 2001. Quantitative Flood Forecasting Using Multisensor Data and Neural Networks. Journal of Hydrology, 246, 45–62.
Rumelhart, D. E., Durbin, R., Golden, R., and Chauvin, Y. 1995. Backpropagation: The Basic Theory. Backpropagation: Theory, Architectures, and Applications, Rumelhart, D.E., Chauvin, Y., eds, Lawrence Erlbaum Associates, Publishers, Hillsdale, 1–34.
The MathWorks, Inc. 1998. Neural Network Toolbox for use with Matlab 5.3/version 3, The MathWorks, Natick, MA.
Stearns, J., Tissot, P.E., Michaud, P., Colllins, W.G., and Patrick, A.R., “Comparison of MesoEta Wind Forecasts with TCOON Measurements along the Coast of Texas” Proceedings of the 19th AMS Conference on Weather Analysis and Forecasting/15th AMS Conference on Numerical Weather Prediction, 12–16 August 2002, San Antonio, Texas, accepted.
NOS Procedures for Developing and Implementing Operational Nowcast and forecast Systems for PORTS, National Oceanic and Atmospheric Administration, U.S. Department of Commerce, 1999
Kemeny, J., Snell J., Mathematical Models in Social Sciences, The MIT Press, 1972.
Sadovski L.E., Sadovski A.L., Mathematics and Sports, American Mathematical Society, RI, 1993.
Arrow K.J., Social Choice and Individual Values, Wiley&Sons, NY, 1963.
Sadovski A.L., Preference Ranking and Decisions Based on Fuzzy Expert Information, in “Advances in Fuzzy Systems and Evolutionary Computation”, World Scientific Engineering Society Press, USA, 2001
Michaud, P., Jeffress, G. A., Dannelly, R. S., Steidley, C, Real-Time Data collection and the Texas Coastal Ocean Observation Network, Instrument Society of America, proceedings of Emerging Technologies Conference, Houston, Texas, 2001
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sadovski, A.L., Steidley, C., Michaud, P., Tissot, P. (2003). Developing a Goodness Criteria for Tide Predictions Based on Fuzzy Preference Ranking. In: Chung, P.W.H., Hinde, C., Ali, M. (eds) Developments in Applied Artificial Intelligence. IEA/AIE 2003. Lecture Notes in Computer Science(), vol 2718. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45034-3_40
Download citation
DOI: https://doi.org/10.1007/3-540-45034-3_40
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40455-2
Online ISBN: 978-3-540-45034-4
eBook Packages: Springer Book Archive