Comparison of ANN and SVR based models in sea level prediction for the Black Sea coast of Sinop

Abstract

Seawater level oscillations are very critical to coastal construction, flood prevention and human living conditions. However, it is difficult to accurately project the daily future for seawater level due to the effects of wind, precipitation and other atmospheric conditions. For this reason, in this paper, artificial intelligence (AI) based Artificial Neural Networks (ANN) and Support Vector Regression (SVR) methods are applied for the estimation of seawater level in Sinop Coast. In addition, Multiple Linear Regression (MLR) is used as a benchmarking model. In this study, coefficient of determination (R2) and root mean square error (RMSE) were applied as model evaluation criteria. Besides, 15 minutes (approximately 22 months) sea water level data of Sinop Station were collected and used as is. The findings revealed that the ANN model can predict the water level for 1st, 2nd, 3rd, 4th days with correlation coefficients (R2) of 0.84, 0.67, 0.64, 0.63, respectively, and the SVR model can predict for 1st, 2nd days with correlation coefficients (R2) of 0.86, 0.66, respectively.