Forecasting Sea Water Levels at Mukho Station, South Korea Using Soft Computing Techniques

The accuracy of three different data-driven methods, namely, Gene Expression Programming (GEP), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Networks (ANN), is investigated for hourly sea water level prediction at the Mukho Station in the East Sea (Sea of Japan). Current and four previous level measurements are used as input variables to predict sea water levels up to 1, 24, 48, 72, 96 and 120 hours ahead. Three statistical evaluation parameters, namely, the correlation coefficient, the root mean square error and the scatter index are used to assess how the models perform. Investigation results indicate that, when compared to measurements, for +1h prediction interval, all three models perform well (with average values of R = 0.993, RMSE = 1.3 cm and SI = 0.04), with slightly better results produced by the ANNs and ANFIS, while increasing the prediction interval degrades model performance.