An adaptive neuro-fuzzy inference system for sea level prediction considering tide-generating forces and oceanic thermal expansion

Li Ching Lin*, Hsien-Kuo Chang

*Corresponding author for this work

Research output: Contribution to journalArticle

10 Scopus citations

Abstract

The paper presents an adaptive neuro fuzzy inference system for predicting sea level considering tide-generating forces and oceanic thermal expansion assuming a model of sea level dependence on sea surface temperature. The proposed model named TGFT-FN (Tide-Generating Forces considering sea surface Temperature and Fuzzy Neuro-network system) is applied to predict tides at five tide gauge sites located in Taiwan and has the root mean square of error of about 7.3 - 15.0 cm. The capability of TGFT-FN model is superior in sea level prediction than the previous TGF-NN model developed by Chang and Lin (2006) that considers the tide-generating forces only. The TGFT-FN model is employed to train and predict the sea level of Hua-Lien station, and is also appropriate for the same prediction at the tide gauge sites next to Hua-Lien station.

Original languageEnglish
Pages (from-to)163-172
Number of pages10
JournalTerrestrial, Atmospheric and Oceanic Sciences
Volume19
Issue number1-2
DOIs
StatePublished - 1 Apr 2008

Keywords

  • Adaptive neuro-fuzzy inference system
  • Sea surface temperature
  • Tide-generating force

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