American Society of Civil Engineers


Prediction of Short-Term Operational Water Levels Using an Adaptive Neuro-Fuzzy Inference System


by Jalal Shiri, Ph.D., (corresponding author), S.M.ASCE, (Student, Water Engineering Dept., Univ. of Tabriz, IR 51664 Tabriz, Iran. E-mail: j_shiri2005@yahoo.com), Oleg Makarynskyy, (Senior Associate Scientist, URS Australia, 16/240 Queen St., Brisbane 4000, Australia.), Ozgur Kisi, (Professor, Engineering Faculty, Civil Engineering Dept., Hydraulics Divisions, Erciyes Univ., Kayseri, Turkey.), Willy Dierickx, (Senior Research Officer, Geraardsbergsesteenweg 18, 9860 Oosterzele, Belgium.), and Ahmad Fakheri Fard, (Professor, Water Engineering Dept., Univ. of Tabriz, IR 51664 Tabriz, Iran.)

Journal of Waterway, Port, Coastal and Ocean Engineering, Vol. 137, No. 6, November/December 2011, pp. 344-354, (doi:  http://dx.doi.org/10.1061/(ASCE)WW.1943-5460.0000097)

     Access full text
     Purchase Subscription
     Permissions for Reuse  

Document type: Technical Note
Abstract: Sea level estimates are important in many coastal applications and port activities. This paper investigates the ability of a neuro-fuzzy (NF) model to predict sea level variations at a tide gauge site in the Hillarys Boat Harbour, Western Australia. In the first part of the study, previously recorded sea levels were used as input to estimate current sea levels. The results showed an acceptable level of NF model accuracy. In the second part of the study, NF models were implemented to forecast sea levels averaged over 12- and 24-h time periods, three time steps ahead. The NF forecasts were compared with those of artificial neural networks (ANNs) for the same data set. The results show that the NF approach performed better than the ANN in half-daily 12-, 24-, and 36-h sea level predictions. The traditional linear regression and autoregressive models were also tested for comparison, and they demonstrated their inferiority to the results of other techniques.


ASCE Subject Headings:
Sea level
Predictions
Neural networks
Regression analysis
Harbors
Australia

Author Keywords:
Sea level prediction
Artificial neural network
Regression
Hillarys Boat Harbour