基于相空间重构的神经网络风暴潮增水预测方法

Storm surge prediction method of neural network based on phase space reconstruction

风暴潮增水的准确预测对于国民生产、防灾减灾有重大意义。本文提出一种基于相空间重构的神经网络风暴潮增水预测方法,即使用单站风暴潮增水数据重构出与之相关的相空间,然后使用BP神经网络模型拟合该相空间的空间结构。将该模型用于库克斯港风暴潮增水预测,结果表明:该模型应用在风暴潮增水时间序列的预测中是合理、可行的,并具有较高的精度。此外,使用db10小波函数对原始余水位数据进行降噪处理可以显著地提高模型的预测精度。

Accurate prediction of storm surge is significantly meaningful to the national production and disaster prevention and mitigation. The storm surge prediction model of BP neural network based on phase space was purposed in this paper, through combining reconstruction phase space with BP neural network. A phase space is reconstructed with the storm surge data and fitted with BP neural network model. The model is used to predict storm surge in Cuxhaven. The result of calculation shows the model is feasible, reasonable and highly precise. The prediction accuracy can been markedly improved through data de-noising.