基于递归神经网络的风暴潮增水预测

风暴潮增水的准确预测能极大地减少人员伤害和经济损失，具有重要的实用价值。传统的风暴潮预报方法主要包括经验和数值预报，很难建立起相对准确的模型。现有的基于机器学习风暴潮预报方法大都只提取出静态数据间的关系，并没有充分挖掘出风暴潮数据背后的时序关联特性。文中提出了一种基于递归神经网络的风暴潮增水预测方法。本文对风暴潮时序数据进行特定的处理，并设计合适结构的递归神经网络，从而完成时序数据的预测。相较于传统的BP神经网络，递归神经网络能更好地应对时序数据的预测问题。将该方法用于潍坊水站的增水预测中，结果表明，相对于BP神经网络，递归神经网络能得到更好的预测结果，误差更小。

Prediction of storm surge based on recurrent neural network

Accurately forecasting storm surges can greatly reduce personnel injuries and economic losses, and so has great practical value. Traditional methods for predicting storm surge mainly involve experience and numerical forecasting, which makes it very hard to establish accurate models. Most of today’s storm surge forecast methods based on machine learning only extract the relationships among static data and fail to identify the relevant time series properties of these data. In this paper, we propose a storm surge forecast method based on the recurrent neural network. The storm surge data is rearranged with particular treatments, and an appropriate recurrent neural network is designed to perform the prediction of the time series. Compared with traditional BP neural networks, the recurrent neural network can better forecast time series data. In this study, we used a recurrent neural network to predict surges at the Weifang gauge station. The results show that the recurrent neural network produces a better prediction with a smaller error than the BP neural network.