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This paper presents a neural network model to estimate wave spectra from wave parameters by the zero-crossing analysis, maximum, significant, and average wave heights and their corresponding wave periods. The present model was trained with the wave data in the JES (Japan/East Sea), and then applied to wave measurements in the Yellow Sea and the South Sea for its feasibility at other seas. According to the present study, the predicted wave spectra well agree with the measurements, even the wave data not in model training. This comparison shows that the accuracy of the present model is comparable to that of JONSWAP wave spectrum. Furthermore, the present model was employed to the wave hindcasting to see its applicability, while JONSWAP wave spectrum also being applied. The significant wave heights from both numerical simulations well agree with the measured ones. However, the average RMSE of significant wave period with the present neural network was 7.08 % smaller than the other, and its Pearson's correlation coefficient is 6.33 % larger on average. From this, it can be seen that the present model was well applied to the wave hindcasting without considering the local wave measurements. • This paper presents a neural network model to estimate wave spectra from wave parameters by the zero-crossing analysis, maximum, significant, and average heights and the corresponding wave periods. • The wave spectra predicted by the present model well agree with the measured ones. The accuracy of present model is comparable to that of JONSWAP wave spectrum with Hwang et al.'s (2021) peak enhancement factor, comparing them. • The present model was employed to the wave hindcasting in Young-il bay to see its applicability. The numerical simulation with incident waves by JONSWAP wave spectrum was also carried out to compare the applicability of both models. • The numerical results show that the significant wave heights from both models are similar to each other. However, the significant wave period with the present model is more accurate than the other. [ABSTRACT FROM AUTHOR]

The progress of studies on ocean waves using high-frequency (HF) ocean radars is reviewed. All of the free-wave components are related to the Doppler spectrum of the HF radar. Ocean wave spectra can be estimated using HF radars by various methods, such as the semiempirical method, parameter fitting method, linear inversion method, and nonlinear inversion method . The semiempirical method is widely used for estimating wave parameters by evaluating unknown factors in the equation relating the Doppler spectrum and wave parameters. The parameter fitting method is adopted in broad beam radar systems. The linear inversion method is a well-known method to estimate a directional spectrum by approximating the nonlinear equation that relates the Doppler spectrum and wave spectrum to a linear equation. The nonlinear inversion method has been extensively studied in Japan. The accuracy of wave estimation using HF radars, and thus, coastal wave prediction, will be improved by selecting high signal-to-noise ratio Doppler spectra for analyses. [ABSTRACT FROM AUTHOR]