[1]LEI Sen,SHI Zhenwei,SHI Tianyang,et al.Prediction of storm surge based on recurrent neural network[J].CAAI Transactions on Intelligent Systems,2017,12(5):640-644.[doi:10.11992/tis.201706015]
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Prediction of storm surge based on recurrent neural network

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 12 Number of periods: 2017 5 Page number: 640-644 Column: 学术论文—智能系统 Public date: 2017-10-25
Title:
Prediction of storm surge based on recurrent neural network
Author(s):
LEI Sen1;  SHI Zhenwei1;  SHI Tianyang1;  GAO Song2;  LI Yaru2;  ZHONG Shan2
1. Image Processing Center, School of Astronautics, Beihang University, Beijing 100191, China;
2. Beihai Forecast Center of State Oceanic Administration, Qingdao 266000, China
Keywords:
storm surge; prediction; numerical forecast; machine learning; static data; temporal properties; BP neural networks; recurrent neural network
CLC:
TP751
DOI:
10.11992/tis.201706015
Abstract:
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.
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