基于EEMD-BP组合模型的区域海平面变化多尺度预测  

Multi-scale prediction of regional sea level variations based on EEMD-BP combined model

作　　者：赵健[1,2] 樊彦国 张音[3] ZHAO Jian;FAN Yanguo;ZHANG Yin(College of Ocean and Space Information,China University of Petroleum(East China),Qingdao 266580,China;Laboratory for Marine Mineral Resources,National Laboratory for Marine Science and Technology(Qingdao),Qingdao 266071,China;School of Economics and Management,China University of Petroleum(East China),Qingdao 266580,China)

机构地区：[1]中国石油大学(华东)海洋与空间信息学院,青岛266580 [2]青岛海洋科学与技术国家实验室海洋矿产资源评价与探测技术功能实验室,青岛266071 [3]中国石油大学(华东)经济管理学院,青岛266580

出　　处：《系统工程理论与实践》2019年第10期2713-2722,共10页Systems Engineering-Theory & Practice

基　　金：中央高校基本科研业务费专项资金(18CX02066A);山东省自然科学基金(ZR2014DQ008);国家自然科学基金(71602187)~~

摘　　要：海平面变化具有非线性、非平稳性以及多时间尺度变化特性,传统的基于时间序列的统计预测模型对于这种变化的预测存在较大局限性,预测结果不够理想.结合集合经验模式分解(ensemble empirical mode decomposition,EEMD)与BP(back propagation)神经网络,本文提出一种改进的海平面变化多尺度预测方法--EEMD-BP组合模型.首先利用EEMD分解提取海平面变化序列中具有物理含义的信号并降低噪声影响,得到不同尺度的多个固有模式函数(intrinsic mode functions,IMFs),确定出海平面变化中隐含的周期及趋势;然后利用BP神经网络分别对不同尺度IMF分量建立预测模型分析其未来变化趋势,最后将各IMF分量的预测结果重构,得到海平面变化序列的最终预测值.结果显示:EEMD分解能有效提取海平面变化中隐含的多时间尺度信号,神经网络能较好地预测不同尺度海平面变化趋势.相对于直接利用BP神经网络进行预测(R=0.74,RMSE=37.51 mm,MAE=48.02 mm),EEMD-BP组合模型预测精度显著提高(R=0.88,RMSE=29.23 mm,MAE=37.50 mm).EEMD-BP组合模型首先对海平面变化序列进行平稳化、降噪等处理,再对分解后的不同尺度时间序列分别进行预测,能够有效提高预测精度,为区域海平面变化预测研究提供了一种新方法.Sea level variability has the characteristics of non-linear,non-stationary and multi-time scale,and the traditional prediction method based on statistical model has some limitations,thence the prediction results are not ideal.Based on the ensemble empirical mode decomposition(EEMD)and back propagation(BP)neural network,the paper proposes an improved sea level multi-scale prediction method,namely,EEMD-BP combined model.Firstly,the multi-scale frequency oscillatory modes(intrinsic mode functions,IMFs)representing different oceanic processes are extracted by EEMD from the highest frequency to the lowest frequency oscillating mode.The remaining non-oscillating mode is the residual,or the sea level trend.Secondly,BP neural network is used to establish prediction models for different scale IMF to analyze their future trends,and each IMF is used as an input factor of the BP neural network separately.Finally,the prediction results of each IMF with BP neural network are reconstructed to obtain the final sea level prediction results.The results showed that EEMD is particularly suitable for analyzing non-linear and non-stationary time series,and BP neural network is applicable for regional sea level prediction at different scales.Comparing with the prediction by BP neural network directly(R=0.74,RMSE=37.51 mm,MAE=48.02 mm),the EEMD-BP combined method improved prediction accuracy significantly(R=0.88,RMSE=29.23 min,MAE=37.50 mm).The results showed that EEMD-BP combined model offers a new method for regional sea level change prediction and is especially suitable for the prediction of non-linear time series.

关 键 词：海平面变化 预测 EEMD BP神经网络 

分 类 号：P722.6[天文地球—海洋科学]