A SOM−RBFnn-Based Calibration Algorithm of Modeled Significant Wave Height for Nearshore Areas

Abstract

In this paper, a calibration algorithm for forecasting the significant wave height (SWH) in nearshore areas is proposed, based on artificial neural networks. The algorithm has two features: first, it is based on SOM−BRFnn (self−organizing map–radial basis function neural network) to better reflect the clustering characteristics of the input parameters regarding wind and wave. In addition, the high-frequency variation part and the low-frequency variation part of SWH are separated by a threshold of 24 h to better describe the diurnal variation of SWH under the influence of tidal current. The algorithm is applied to the nearshore region of Nan-ao Island in the northeastern South China Sea. The results show that the algorithm can effectively correct the modeling results of nearshore SWH. Compared with the original outputs of the ERA5 model, the correlation coefficient is increased from 0.472 to 0.774, the root mean square error is reduced from 0.252 m to 0.103 m, and the mean relative error is reduced from 41% to 17.6%, respectively. Further analysis indicates that the frequency division is crucial in realizing the correction of the high-frequency variation of SWH. The results have reference significance for the application of wave numerical models in coastal areas.