To explore new operational forecasting methods of waves, a forecasting model for wave heights at three stations in the Bohai Sea has been developed. This model is based on long short-term memory (LSTM) neural network with sea surface wind and wave heights as training samples. The prediction performance of the model is evaluated, and the error analysis shows that when using the same set of numerically predicted sea surface wind as input, the prediction error produced by the proposed LSTM model at Sta. N01 is 20%, 18% and 23% lower than the conventional numerical wave models in terms of the total root mean square error (RMSE), scatter index (SI) and mean absolute error (MAE), respectively. Particularly, for significant wave height in the range of 3–5 m, the prediction accuracy of the LSTM model is improved the most remarkably, with RMSE, SI and MAE all decreasing by 24%. It is also evident that the numbers of hidden neurons, the numbers of buoys used and the time length of training samples all have impact on the prediction accuracy. However, the prediction does not necessary improve with the increase of number of hidden neurons or number of buoys used. The experiment trained by data with the longest time length is found to perform the best overall compared to other experiments with a shorter time length for training. Overall, long short-term memory neural network was proved to be a very promising method for future development and applications in wave forecasting.

为了探索新的海浪运行预报方法，建立了渤海三个站点海浪高度的预报模型。该模型基于长短期记忆（LSTM）神经网络，其中海面风和浪高作为训练样本。对模型的预测性能进行了评估，误差分析表明，当使用同一组数值预测的海面风作为输入时，建议的LSTM模型在Sta产生的预测误差。就总的均方根误差（RMSE），散射指数（SI）和平均绝对误差（MAE）而言，N01分别比常规的数值波动模型低20％，18％和23％。特别是，对于3至5 m范围内的显着波高，使用RMSE可以显着提高LSTM模型的预测精度，SI和MAE均下降了24％。同样明显的是，隐藏神经元的数量，所使用的浮标的数量以及训练样本的时间长度都对预测准确性有影响。但是，随着隐藏神经元数量或所使用浮标数量的增加，预测并不一定会得到改善。与具有较短时间长度的其他实验相比，由具有最长时间长度的数据训练的实验发现整体效果最佳。总体而言，长期的短期记忆神经网络被证明是一种非常有前途的方法，可用于海浪预测的未来发展和应用。预测不一定会随着隐藏神经元数量或所使用浮标数量的增加而改善。与具有较短时间长度的其他实验相比，由具有最长时间长度的数据训练的实验发现整体效果最佳。总体而言，长期的短期记忆神经网络被证明是一种非常有前途的方法，可用于海浪预测的未来发展和应用。预测不一定会随着隐藏神经元数量或所使用浮标数量的增加而改善。与具有较短时间长度的其他实验相比，由具有最长时间长度的数据训练的实验发现整体效果最佳。总体而言，长期的短期记忆神经网络被证明是一种非常有前途的方法，可用于海浪预测的未来发展和应用。