Analysis of the Gulf of Mexico water levels as random signals

Alexey L. Sadovski, G. Beate Zimmer, Blair Sterba-Boatwright, Philippe Tissot, Rafic A. Bachnak

Research output: Contribution to journalArticlepeer-review


This paper deals with estimates for the best water level predictions for the shallow waters of the Gulf of Mexico. The predictions are made by Artificial Neural Networks (ANNs) and Statistical Modeling. In this paper we use elements of the theory of stochastic to evaluate the quality of forecasts, the stochastic properties of the inputs as well as the given goodness criteria of predictions. As a result of such investigation we can outline limitations of different methods used for predictions of water levels in the bays and estuaries of the Texas coast.

Original languageEnglish (US)
Pages (from-to)1234-1240
Number of pages7
JournalWSEAS Transactions on Mathematics
Issue number11
StatePublished - Nov 2006

All Science Journal Classification (ASJC) codes

  • Mathematics (miscellaneous)
  • Computational Mathematics
  • Computer Science (miscellaneous)


Dive into the research topics of 'Analysis of the Gulf of Mexico water levels as random signals'. Together they form a unique fingerprint.

Cite this