Real-time prediction of water level change using adaptive neuro-fuzzy inference system

Mosbeh R. Kaloop, Mohammed El-Diasty, Jong Wan Hu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)


Accurate water levels modelling and prediction is essential for maritime applications. Water prediction is traditionally developed using the least-squares-based harmonic analysis method based on water level change (WLC) measurements. If long water level measurements are not obtained from the tide gauge, accurate water levels prediction cannot be estimated. To overcome the above limitations, the wavelet neural network (WNN) has recently been developed for the WLC prediction from short water level measurements. However, a new adaptive neuro-fuzzy inference system (ANFIS) model is proposed and developed in this paper. The ANFIS model is utilized to predict and select the WLC models of one month of hourly WLC for Yarmouth, Sain-John and Charlottetown stations in Canadian waters and compared with the current-state-of-the-art WNN model. The statistical analysis is applied to analyse the performance of the developed model in training and testing stages. The results showed an accurate modelling level using ANFIS technique for each station in training and testing stage. A comparison between the developed ANFIS method and the current-state-of-the-art WNN method shows that the accuracy of the developed ANFIS model is superior to the current-state-of-the-art model by 21.5% in average.

Original languageEnglish
Pages (from-to)1320-1332
Number of pages13
JournalGeomatics, Natural Hazards and Risk
Issue number2
Publication statusPublished - Dec 15 2017


  • fuzzy
  • prediction
  • Water level
  • wavelet

ASJC Scopus subject areas

  • General Environmental Science
  • General Earth and Planetary Sciences

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