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

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.