基于递归神经网络的风暴潮增水预测
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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。
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.
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.
引文
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[2]高清清,曹兵,高鑫鑫,等.南通沿海台风风暴潮分析及其经验预报初探[J].海洋预报,2014,31(1):29-35.GAO Qingqing,CAO Bing,GAO Xinxin,et al.Analysis of the typhoon storm surge in the Nantong coastal zone and the forecasting formulas[J].Marine forecasts,2014,31(1):29-35.
[3]曾德美.青岛港风暴潮经验统计预报[J].海洋预报,1992,9(3):66-73.ZENG Demei.A statistical forecasting of storm surge in qingdao harbor[J].Marine forecasts,1992,9(3):66-73.
[4]谢亚力,黄世昌.钱塘江河口风暴潮经验预报[J].海洋预报,2006,23(1):54-58.XIE Yali,HUANG Shichang.Effects on watercourse and hydrological factor in downstrean nanxi river due to water diversion[J].Marine forecasts,2006,23(1):54-58.
[5]朱建荣,朱首贤.ECOM模式的改进及在长江河口、杭州湾及邻近海区的应用[J].海洋与湖沼,2003,34(4):364-373.ZHU Jianrong,ZHU Shouxian.Improvement of the ECOMwith application to the changjiang river estuary,Hangzhou bay and adjacent waters[J].Oceanologia Et limnologia sinica,2003,34(4):364-373.
[6]黄世昌,李玉成,赵鑫,等.浙江沿海超强台风作用下风暴潮增水数值分析[J].海洋工程,2008,26(3):58-64.HUANG Shichang,LI Yucheng,ZHAO Xin,et al.Numerical analysis of storm surge due to a super typhoon in coastal region of Zhejiang province[J].The ocean engineering,2008,26(3):58-64.
[7]王培涛,于福江,刘秋兴,等.福建沿海精细化台风风暴潮集合数值预报技术研究及应用[J].海洋预报,2010,27(5):7-15.
[8]LEE T L.Neural network prediction of a storm surge[J].Ocean engineering,2006,33(3):483-494.
[9]RAJASEKARAN S,GAYATHRI S,LEE T L,et al.Support vector regression methodology for storm surge predictions[J].Ocean engineering,2008,35(16):1578-1587.
[10]WILLIAMS R J,ZIPSER D.A learning algorithm for continually running fully recurrent neural networks[J].Neural computation,1989,1(2):270-280.
[11]ELMAN J L.Finding structure in time[J].Cognitive science,1990,14(2):179-211.
[12]BOTVINICK M M,PLAUT D C.Short-term memory for serial order:a recurrent neural network model[J].Psychological review,2006,113(2):201-233.
[13]SAINATH T N,KINGSBURY B,SAON G,et al.Deep convolutional neural networks for large-scale speech tasks[J].Neural networks,2015,64:39-48.
[14]SARIKAYA R,HINTON G E,DEORAS A,et al.Application of deep belief networks for natural language understanding[J].IEEE transactions on audio,speech,and language processing,2014,22(4):778-784.
[15]KRIZHEVSKY A,SUTSKEVER I,HINTON G E,et al.Image Net classification with deep convolutional neural networks[C]//International Conference on Neural Information Processing Systems.Nevada,America,2012:1097-1105.
[16]SVOZIL D,KVASNICKA V,POSPICHAL J,et al.Introduction to multi-layer feed-forward neural networks[J].Chemometrics and intelligent laboratory systems,1997,39(1):43-62.
[17]HORNIK K,STINCHCOMBE M B,WHITE H,et al.Multilayer feedforward networks are universal approximators[J].Neural networks,1989,2(5):359-366.
[18]RUMELHART D E,HINTON G E,WILLIAMS R J,et al.Learning representations by back-propagating errors[J].Nature,1988,323(6088):533-536.
[19]KOSMATOPOULOS E B,POLYCARPOU M M,CHRISTODOULOUM A,et al.High-order neural network structures for identification of dynamical systems[J].IEEE transactions on neural networks,1995,6(2):422-431.
[20]WERBOS P J.Backpropagation through time:what it does and how to do it[J].Proceedings of the IEEE,1990,78(10):1550-1560.
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