College of Ocean and Space Information, China University of Petroleum (East China), Qingdao 266580, China.
Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266071, China.
Sensors (Basel). 2019 Nov 2;19(21):4770. doi: 10.3390/s19214770.
Sea level change is a key indicator of climate change, and the prediction of sea level rise is one of most important scientific issues. In this paper, the gridded sea level anomaly (SLA) data from satellite altimetry are used to analyze the sea level variations around Shandong Peninsula from 1993 to 2016. Based on the Complete Ensemble Empirical Mode Decomposition (CEEMD) method and Radial Basis Function (RBF) network, the paper proposes an improved sea level multi-scale prediction approach, namely, CEEMD-RBF combined model. Firstly, the multi-scale frequency oscillatory modes (intrinsic mode functions (IMFs)) representing different oceanic processes are extracted by CEEMD from the highest frequency to the lowest frequency oscillating mode. Secondly, RBF network is used to establish prediction models for various IMF components to predict their future trends, and each IMF is used as an input factor of the RBF network separately. Finally, the prediction results of each IMF component with RBF network are reconstructed to obtain the final predictions of sea level anomalies. The results shows that CEEMD is particularly suitable for analyzing nonlinear and non-stationary time series and RBF network is applicable for regional sea level prediction at different scales.
海平面变化是气候变化的一个关键指标，而海平面上升的预测是最重要的科学问题之一。本文利用卫星测高得到的网格化海平面距平（SLA）数据，分析了 1993 年至 2016 年山东半岛周边海平面的变化。基于完备集合经验模态分解（CEEMD）方法和径向基函数（RBF）网络，提出了一种改进的多尺度海平面预测方法，即 CEEMD-RBF 组合模型。首先，CEEMD 从最高频率到最低频率的振荡模式中提取出代表不同海洋过程的多尺度频率振荡模态（本征模态函数（IMF））。其次，利用 RBF 网络分别为各 IMF 分量建立预测模型，以预测其未来趋势，并将每个 IMF 作为 RBF 网络的一个输入因子。最后，通过 RBF 网络对各 IMF 分量的预测结果进行重构，得到海平面距平的最终预测结果。结果表明，CEEMD 特别适用于分析非线性和非平稳时间序列，RBF 网络适用于不同尺度的区域海平面预测。
