Hybrid Wavelet-SVM Method to Predict the Occurrence of Abnormal Waves

Document Type : Research Article


1 Ph.D. Student, Faculty of Marine Technology, Amirkabir University of Technology, Tehran, Iran

2 Associate Professor, Faculty of Marine Technology, Amirkabir University of Technology, Tehran, Iran

3 Professor, Faculty of Marine Technology, Amirkabir University of Technology, Tehran, Iran.


Abnormal waves are extremely large and unusual which rarely occur, but cause serious damages. Various factors such as extreme storms, particular topography of the seabed, marine currents, wave-wave interaction with different wavelengths and frequencies may cause wave occurrence and transformation. The main objective of this study is to propose a new hybrid approach to predict the occurrence of abnormal waves using wavelet transform and support vector machines (SVM) classifier based on meteorological data. The data sets used in this paper are from two major hurricanes Dean 2007 and Irene 2011 at four locations namely, 41004 and41041 in the Gulf of Mexico. To predict the occurrence of abnormal waves, at the first extreme waves are detected using wavelet transform. The outputs of this method are considered as SVM classifier targets. Wavelet transform is applied on the significant wave height data samples. The abnormal waves are readily identified from the wavelet spectrum as an area of high energy. The inputs of SVM classifier models are historical metrological data, including: Wind direction (WDIR), Wind speed (WSPD), Sea level pressure (PRES), Air temperature (ATMP), and Sea surface temperature (WTMP). The experiment results show that the proposed method is able to predict the occurrence of extreme wave heights with height accuracy.


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  • Receive Date: 04 August 2017
  • Revise Date: 30 December 2017
  • Accept Date: 31 December 2017
  • First Publish Date: 21 March 2018