Wave height prediction with single input parameter by using regression methods

ABSTRACT

Regression methods can be used for the prediction of parameters under the influence of environmental factors. An effective wave height prediction is important for calculating the wave potential. In the literature, the wave height prediction is generally performed by using the input parameters obtained with different physical effects, with different sensors such as water temperature, daily temperature, daily humidity, wind speed, etc. In this study, the prediction of offshore wave height has been proposed to achieve with a single parameter which is the flow velocity and contains the same physical effects of the wave unlike literature. The effect of different values on different depth of flow velocity has also been investigated by using Relief algorithm. Linear, Decision Tree, Support Vector Machine, Ensemble, and Gaussian Regression models have been studied by using different values of the specified parameters of them for two stations of Mediterranean Sea in Turkey. Various evaluation criteria (MSE, RMSE, MAE, and R_square) have also been utilized to validate the performance of the wave height prediction. The best prediction performances for B₁ and B₂ buoys are obtained with R_square values as 0.866 and 0.954, respectively. These results prove the achievement of the proposed.

KEYWORDS:

• Wave height
• prediction
• regression
• relief
• flow velocity

Acknowledgments

Used data in this study have been carried out from Turkish State Meteorological Service. Thanks for their support in obtaining the observed data.

The authors sincerely thank the Editor-In-Chief, the Associate Editor, and all the reviewers for their useful and gracious comments, which improved the clarity of the final manuscript.

Additional information

Notes on contributors

Narin Karabulut

Narin Karabulut received her master degree in Mechanic Department of Usak University. She is currently a Ph.D student in Mechatronics Engineering Department, Technology Faculty, Firat University. Her research interests are Materials and Methods,  Intelligent Control Techniques, Optimization Methods, Signal and Image Processing

Gonca Ozmen Koca

Gonca Ozmen Koca received her Ph.D degree in Electric and Electronic Engineering Department of Firat University. She is currently an Assistant Professor in the Control Division of Mechatronics Engineering Department, Technology Faculty, Firat University. Her research areas cover Control Systems, Path Planning, Biomimetic Systems, Intelligent Control Techniques, Optimization Methods.