[1]雷森,史振威,石天阳,等.基于递归神经网络的风暴潮增水预测[J].智能系统学报,2017,12(05):640-644.[doi:10.11992/tis.201706015]
 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(05):640-644.[doi:10.11992/tis.201706015]
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基于递归神经网络的风暴潮增水预测(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第12卷
期数:
2017年05期
页码:
640-644
栏目:
出版日期:
2017-10-25

文章信息/Info

Title:
Prediction of storm surge based on recurrent neural network
作者:
雷森1 史振威1 石天阳1 高松2 李亚茹2 钟山2
1. 北京航空航天大学 宇航学院图像处理中心, 北京 100191;
2. 国家海洋局 北海预报中心, 山东 青岛 266000
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
关键词:
风暴潮增水预测数值预报机器学习静态数据时序特性BP神经网络递归神经网络
Keywords:
storm surgepredictionnumerical forecastmachine learningstatic datatemporal propertiesBP neural networksrecurrent neural network
分类号:
TP751
DOI:
10.11992/tis.201706015
摘要:
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。
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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备注/Memo

备注/Memo:
收稿日期:2017-06-07。
基金项目:国家自然科学基金项目(61671037).
作者简介:雷森,男,1992年生,博士研究生,主要研究方向为图像处理、机器学习、遥感影像质量提升;史振威,男,1977年生,教授,博士生导师,博士,主要研究方向为图像处理、模式识别、机器学习、遥感影像处理。发表SCI国际期刊检索论文70余篇;石天阳,男,1994年生,硕士研究生,主要研究方向为机器学习和人工智能。
通讯作者:史振威.E-mail:shizhenwei@buaa.edu.cn
更新日期/Last Update: 2017-10-25