摘要
基于时间序列的统计预测模型是现阶段海平面高度预测的主要手段之一，然而海平面变化机理复杂，传统方法对于非平稳非线性的时间序列预测存在较大局限性，预测精度有待进一步提高。本文基于闸坡站长时间（1959-2011年）月均验潮序列，结合集合经验模态分析（Ensemble Empirical Mode Decomposition，简称EEMD）与BP（Back Propagation）神经网络方法，提出一种改进的区域海平面变化趋势预测方法——EEMD-BP建模。本研究首先利用EEMD方法对原始序列进行分解，根据验潮序列中隐含的各个信号的不同频谱特征生成多个本征模函数（IntrinsicModeFunction，简称IMF），达到将时间序列平稳化，提高信噪比的效果。然后由各IMF作为BP神经网络的输入因子，分别预测各IMF的未来变化趋势，最后将输出结果重建得到原始序列的预测值。结果显示，EEMD能有效提取序列中隐含的多时间尺度信号，神经网络能较好地预测海平面未来变化趋势，相对于直接使用BP神经网络进行海平面变化时间序列预测（R=0．76，RMSE=36．74mm，ME=-3．46），EEMD．BP建模预测精度有显著提高（R=0．89，RMSE=28．16mm，ME=2．31）。说明EEMD．BP建模首先对非平稳非线性时间序列进行平稳化、降噪等处理，再分别对分解后序列进行预测，有利于提高预测精度。该方法为相关区域海平面变化趋势预测研究提供现实参考意义。
The statistical model is one of the primary methods for sea level prediction. However, the conventional methods are still considered insufficient due to the complexity of the unstable and nonlinear sea level time series, and the prediction accuracy needs to be improved. Zhapo tide gauge （21°35＇N, 111°50＇E） is a standard tide gauge with long records in the Pearl River delta, China. In this study the monthly records （01/1959- 12/2011） of Zhapo, which was collected from Global Sea Level Observing System （GLOSS） , was taken as an example, a hybrid method combining the data analysis method ensemble empirical mode decomposition （EEMD） and the artificial neural network （ANN） with back propagation algorithm （BP） was proposed to improve the prediction accuracy of sea level. EEMD extracted the frequency contributions called intrinsic mode functions （IMF） from the targeted time series oscillatory signal according to their unique fluctuation characteristic. 9 IMFs were obtained from the series of Zhapo tide gauge by using EEMD, and 6 of them passed the significance test on the level of 95%, suggesting that these 6 IMFs have statistical meaning. And the corresponding cycles such as seasonal cycle, semi-annual cycle, annual cycle, ENSO event cycle, semi nodical period and nutation cycle and so on are the significant characteristics in sea level change around Zhapo. The 6 IMFs were taken as the input factors of the BP neural network used for prediction. A BP neural network with three layers, which was optimized by setting the essential parameters such as the number of the hidden layers and the number of neurons in each layer, the learning rates, the momentum factor, and the number of training iterations and so on, was adopted in this study for modeling the sea level prediction. By reconstructing the outputs of the BP neural network, the prediction of the original time series was obtained. To evaluate the accuracy of the hybrid method, two predictions produced by the hybrid method and BP neural network directly were conducted in this paper. The results show that EEMD can extract the multiple time scales from the original time series, and the BP neural network is applicable for future sea level prediction. Comparing with the prediction by using BP neural network directly （R = 0. 76, RMSE = 36.74mm, ME = -3.46） , the EEMD-BP hybrid method performs better （R = 0.89, RMSE = 28.16mm, ME = 2.31 ）. The results suggest that EEMD-BP model significantly improves the prediction accuracy, and the preprocessing such as smoothing and denoising is strongly recommended for the prediction of unstable and nonlinear time series. The results of this study also reveals that the sea level has increased at a rate of 2.22mm/a over the period 1959-2011 in the Pearl River delta, and the prediction shows that it is expected to increase to 2.75mm/a by 2050.
出处
《第四纪研究》
CAS
CSCD
北大核心
2015年第2期374-382,共9页
Quaternary Sciences
基金
国家重点基础研究发展规划项目（973项目）（批准号：2012CB95570002）资助
