Derivation of wave spectrum using data driven methods

Abstract

The current techniques of derivation of a wave spectrum from given values of design wave parameters, like significant wave height and average wave period, are fraught with considerable uncertainties. This leaves scope for alternative approaches. The reported work proposes potential applications of two recent data driven methods, namely support vector regression (SVR) and model tree (MT), to obtain the wave spectra. In the present study the above tools were used to estimate wave spectra at two locations: no. 44008 maintained by National Data Buoy Centre (NDBC) in the Gulf of Maine, USA and ‘DS5’ monitored by National Institute of Ocean Technology (NIOT) in Bay of Bengal, India. The choice of these two locations facilitated the comparison of model performances in different geographical areas. The SVR and MT models were developed in order to estimate the wave surface spectral density over a wide range of wave frequencies out of average wave parameters of significant wave height and average zero-cross wave period. The models were trained and tested using randomly selected sea states. Both MT and SVR were able to derive the spectral shapes satisfactorily as reflected in high values of the correlation coefficients and low values of root mean square error and mean square error.