Estimating daily mean sea level heights using artificial neural networks

E. Sertel*, H. K. Cigizoglu, D. U. Sanli

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

33 Citations (Scopus)


The main purpose of this study is to estimate daily mean sea level heights using five different methods, namely the least squares estimation of sea level model, the multilinear regression (MLR) model, and three artificial neural network (ANN) algorithms. Feed forward back propagation (FFBP), radial basis function (RBF), and generalized regression neural network (GRNN) algorithms were used as ANN algorithms. Each method was applied to a data set to investigate the best method for the estimation of daily mean sea level. The measurements from a single tide gauge at Newlyn, obtained between January 1991 and December 2005, were used in the study. Daily mean sea level estimation was carried out considering the precedent 8-day mean sea level data of the same station, the average and standard deviation of each day for a 15-year period, and 6 monthly and yearly periodicities in tidal variations. Results of the study illustrated that the ANN and MLR models provided comparatively better results than the conventional model used for estimating sea level, least squares estimation. FFBP, RBF, and MLR algorithms produced significantly better results than the GRNN method, and the best performance was obtained using the FFBP algorithm. From the graphs and statistics, it is apparent that neural networks and MLR solution can provide reliable results for estimating daily mean sea level.

Original languageEnglish
Pages (from-to)727-734
Number of pages8
JournalJournal of Coastal Research
Issue number3
Publication statusPublished - May 2008


  • Feed forward back propagation
  • Generalized regression neural networks
  • Radial basis function


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