Evaluation of the performance of SVR and ANN Models in Sea Water Level Prediction for the Izmir Coast of the Aegean Sea

Abstract

Forecasting of sea water level is a very important phenomenon for making future projections for flood control, human living conditions and coastal planning. However, it is not easy to estimate sea water level due to the influence of atmospheric conditions. Thus, Artificial Neural Networks (ANN) and Support Vector Regression (SVR) methods are used for the prediction of seawater level of Izmir Coast based on the time series data. Coefficient of determination (R2) and root mean square error (RMSE) are applied as model evaluation criteria in this study. 19 months of seawater level data in the time series were used in this study. The results show that the ANN model can predict the water level with R2 of 0.84 and 0.68 for 1st and 2nd days, respectively, while the SVR model can predict the water level with R2 of 0.83 and 0.69 for 1st and 2nd days, respectively.

Keywords

• ANN
• SVR
• Sea Level Prediction
• Izmir coast
• Aegean Sea

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