Prediction of significant wave height using regressive support vector machines

Abstract Wave parameters prediction is an important issue in coastal and offshore engineering. In this literature, several models and methods are introduced. In the recent years, the well-known soft computing approaches, such as artificial neural networks, fuzzy and adaptive neuro-fuzzy inference systems and etc., have been known as novel methods to form intelligent systems, these approaches has also been used to predict wave parameters, as well. It is not a long time that support vector machine (SVM) is introduced as a strong machine learning and data mining tool. In this paper, it is used to predict significant wave height (Hs). The data set used in this study comprises wave wind data gathered from deep water locations in Lake Michigan. Current wind speed (u) and those belonging up to six previous hours are given as input variables, while the significant wave height is the output parameter. The SVM results are compared with those of artificial neural networks, multi-layer perceptron (MLP) and radial basis function (RBF) models. The results show that SVM can be successfully used for prediction of Hs. Furthermore, comparisons indicate that the error statistics of SVM model marginally outperforms ANN even with much less computational time required.

[1]  Mehmet Özger,et al.  Prediction of wave parameters by using fuzzy logic approach , 2007 .

[2]  Dimitri P. Solomatine,et al.  Machine learning in soil classification , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

[3]  Seyed Jamshid Mousavi,et al.  APPLICATION OF FUZZY INFERENCE SYSTEM IN THE PREDICTION OF WAVE PARAMETERS , 2005 .

[4]  Lutgarde M. C. Buydens,et al.  Using support vector machines for time series prediction , 2003 .

[5]  Mahesh Pal,et al.  Estimation of Discharge and End Depth in Trapezoidal Channel by Support Vector Machines , 2007 .

[6]  M. H. Kazeminezhad,et al.  Hindcasting of wave parameters using different soft computing methods , 2008 .

[7]  S. Sathiya Keerthi,et al.  Improvements to the SMO algorithm for SVM regression , 2000, IEEE Trans. Neural Networks Learn. Syst..

[8]  Michael Blumenstein,et al.  Near-shore swell estimation from a global wind-wave model: Spectral process, linear, and artificial neural network models , 2007 .

[9]  Vijay P. Singh,et al.  Estimation of Removal Efficiency for Settling Basins Using Neural Networks and Support Vector Machines , 2008 .

[10]  L. Buydens,et al.  Facilitating the application of Support Vector Regression by using a universal Pearson VII function based kernel , 2006 .

[11]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[12]  Alexander J. Smola,et al.  Support Vector Regression Machines , 1996, NIPS.

[13]  John C. Platt,et al.  Fast training of support vector machines using sequential minimal optimization, advances in kernel methods , 1999 .

[14]  Dimitri P. Solomatine,et al.  Machine learning in soil classification , 2005 .

[15]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[16]  Dina Makarynska,et al.  Artificial neural networks in wave predictions at the west coast of Portugal , 2005, Comput. Geosci..

[17]  Amir Etemad-Shahidi,et al.  An alternative approach for the prediction of significant wave heights based on classification and regression trees , 2008 .

[18]  Mohamed Mohandes,et al.  Support vector machines for wind speed prediction , 2004 .

[19]  J. D. Agrawal,et al.  On-line wave prediction , 2002 .

[21]  S. Gunn.  Support Vector Machines for Classification and Regression , 1998 .

[22]  I-Fan Chang,et al.  Support vector regression for real-time flood stage forecasting , 2006 .

[23]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[24]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[25]  M. C. Deo,et al.  Neural networks for wave forecasting , 2001 .

[26]  Makarand Deo,et al.  Neural networks in ocean engineering , 2006 .

[27]  Mac McKee,et al.  Multi-time scale stream flow predictions: The support vector machines approach , 2006 .

[28]  O. Makarynskyy,et al.  Improving wave predictions with artificial neural networks , 2004 .