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CLC number: U66

On-line Access: 2016-02-02

Received: 2015-06-01

Revision Accepted: 2015-11-23

Crosschecked: 2016-01-16

Cited: 1

Clicked: 1566

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Wen-yang Duan

http://orcid.org/0000-0002-7811-4986

Li-min Huang

http://orcid.org/0000-0002-7944-2754

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Journal of Zhejiang University SCIENCE A 2016 Vol.17 No.2 P.115-129

http://doi.org/10.1631/jzus.A1500164


A hybrid EMD-AR model for nonlinear and non-stationary wave forecasting


Author(s):  Wen-yang Duan, Li-min Huang, Yang Han, De-tai Huang

Affiliation(s):  Department of Shipbuilding Engineering, Harbin Engineering University, Harbin 150001, China

Corresponding email(s):   huanglimin@hrbeu.edu.cn

Key Words:  Wave forecast, Nonlinear and non-stationary, Autoregressive (AR) model, Empirical mode decomposition (EMD), EMD-AR model


Wen-yang Duan, Li-min Huang, Yang Han, De-tai Huang. A hybrid EMD-AR model for nonlinear and non-stationary wave forecasting[J]. Journal of Zhejiang University Science A, 2016, 17(2): 115-129.

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Abstract: 
Accurate wave forecasting with a couple of hours of warning time offers improvements in safety for maritime operation-related activities. autoregressive (AR) model is an efficient and highly adaptive approach for wave forecasting. However, it is based on linear and stationary theory and hence has limitations in forecasting nonlinear and non-stationary waves. Inspired by the capability of empirical mode decomposition (EMD) technique in handling nonlinear and non-stationary signals, this paper describes the development of a hybrid EMD-AR model for nonlinear and non-stationary wave forecasting. The EMD-AR model was developed by coupling an AR model with the EMD technique. Nonlinearity and non-stationarity were overcome by decomposing the wave time series into several simple components for which the AR model is suitable. The EMD-AR model was implemented using measured significant wave height data from the National Data Buoy Center, USA. Prediction results from various locations consistently show that the hybrid EMD-AR model is superior to the AR model. This demonstrates that the EMD technique is effective in processing nonlinear and non-stationary waves.

The authors compared capabilities of two time series forecasting models, the autoregressive model (AR) and the hybrid (EMD-AR) of the empirical mode decomposition (EMD) and AR, through prediction of long term significant wave height based on three NDBC buoy datasets. It is demonstrated by three indices including root mean square error (RMSE), correlation coefficient, and scatter diagram that the EMD-AR performs better than the AR for wave forecast especially in dealing with phase shifting.

一种用于非线性非平稳波浪极短期预报的复合经验模态分解自回归模型

目的:相对于由能量平衡方程得到的数值预报模型和以神经网络为代表的非线性模型而言,自回归(AR)模型在波浪预报中具有计算效率高、自适应性强和建模所需的样本小等优点,但同时存在局限于平稳线性假设的缺陷。针对非线性非平稳波浪的极短期预报问题,提出一种复合的经验模态分解自回归预报模型,提高波浪预报精度。
创新点:1. 研究非线性非平稳波浪极短期预报问题,提出一种复合的预报方法;2. 基于三个不同地理位置的海洋波浪实测数据对预测模型进行验证,并分析非线性非平稳性对波浪预报结果的影响。
方法:1. 在AR模型中引入经验模态分解(EMD)方法,形成复合的EMD-AR预报模型;2. 分析实测波浪数据的非线性和非平稳性特点,并基于实测波浪数据获得AR模型和EMD-AR模型的预报结果;3. 基于多种预报误差度量分析AR模型和EMD-AR模型的预报性能以及非线性非平稳性对波浪预报结果的影响。
结论:1. 波浪非线性和非平稳性会导致AR预报模型精度降低。预报误差中,幅值上的偏差主要由波浪的非线性引起,而相位上的偏差则是源于波浪的非平稳性;2. EMD方法能够有效地克服波浪非线性和非平稳性对AR模型在精度上所带来的不良影响,在精度上EMD-AR模型的预报结果较AR模型有较大提高。

关键词:波浪预报;非线性和非平稳性;自回归模型;经验模态分解;经验模态分解自回归模型

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