人工神经网络在海浪数值预报中的应用

探讨将人工神经网络技术和传统的数值模式相结合,以期得到一个更有效的海浪预报方法.以第3代海浪模式的模拟结果作为输入,浮标观测资料作为输出,采用人工神经网络进行训练,训练的初步结果显示,人工神经网络可以改进海浪数值模式的预报精度,但在波高比较大时,改进的效果并不令人满意.为此,对观测值大于1.5m时的有效波高进行再训练,从而结果有了进一步的改善.研究结果证明人工神经网络技术可以提高海浪数值预报的精度.

Application of artificial neural network to numerical wave prediction

The objective of this paper is to use an artificial neural network (ANN) model to train the output of a third generation wave model to better forecast the significant wave heights from buoy data. After training, the agreement between the wave model's output and the buoy data generally increases, but there is still significant disagreement when the wave height is at its peak. The significant wave heights bigger than 1.5m are selected to retrain, using the same ANN model, and the resulting improvement in the forecast is obvious since the root mean square error (RMS) between the ANN output and the buoy data decrease from 0.31 m to 0.29 m. The goal of this paper is to investigate the feasibility of using an ANN to improve a wave (model's) numerical wave prediction so as to develop a more accurate wave forecasting system. The results show that an ANN is an useful tool for this purpose.