An EEMD and BP neural network hybrid approach for modeling regional sea level change
文献类型：期刊论文
| 作者 | He, Lei1,2; Chen, Jilong3; Zhang, Yue1; Guo, Tengjiao1; Li, Guosheng1 |
| 刊名 | DESALINATION AND WATER TREATMENT
 |
| 出版日期 | 2018-07-01 |
| 卷号 | 121页码:139-146 |
| ISSN号 | 1944-3994 |
| 关键词 | Regional variations Sea level oscillations Pearl River Delta |
| DOI | 10.5004/dwt.2018.22378 |
| 通讯作者 | Li, Guosheng(ligs@igsnrr.ac.cn) |
| 英文摘要 | Sea level prediction is essential and complicated in the context of climate change. Conventional methods developed for the prediction are still considered insufficient due to the complexity of the nonstationary and nonlinear sea level change. To improve the modeling accuracy of the sea level, this paper proposed a methodology combining the ensemble empirical mode decomposition (EEMD) and the back propagation (BP) neural network for monthly mean sea level record modeling in South China Sea. The results show that the EEMD can extract the signals with physical meanings according to their unique frequencies. The inputs of the BP, defined by the preprocessing of the original time series, turn out to be smoother and more regular, influencing the modeling in a positive way. The good performance of the hybrid method, with higher correlation coefficient (R = 0.89) and lower root square mean error (RMSE = 28.16 mm) between the modeling and the observed data, suggests an improved accuracy on sea level modeling than using the BP directly (with R = 0.76 and RMSE = 36.74 mm). This hybrid method can be further applied to sea level modeling in another region. The results of the study also suggest that the preprocessing of the original time series such as smoothing and denoising is significantly improving the modeling. |
| WOS关键词 | TIME-SERIES ; CHANGE SCENARIOS ; COASTAL ZONES ; TIDE GAUGES ; RISE ; DECOMPOSITION ; VARIABILITY ; ALTIMETRY ; VULNERABILITY ; PREDICTION |
| 资助项目 | National Natural Science Foundation of China[41571041] ; National Natural Science Foundation of China[41601453] ; Opening Fund of Key Laboratory of Poyang Lake Wetland and Watershed Research (Jiangxi Normal University), Ministry of Education[ZK2015001] ; Jiangxi Province Department of Education Science and technology research project[GJJ150306] ; Construction Service Program for Cultivating Unique Institution of the Chinese Academy of Sciences[TSYSJ04] |
| WOS研究方向 | Engineering ; Water Resources |
| 语种 | 英语 |
| 出版者 | DESALINATION PUBL |
| WOS记录号 | WOS:000446586600019 |
| 资助机构 | National Natural Science Foundation of China ; Opening Fund of Key Laboratory of Poyang Lake Wetland and Watershed Research (Jiangxi Normal University), Ministry of Education ; Jiangxi Province Department of Education Science and technology research project ; Construction Service Program for Cultivating Unique Institution of the Chinese Academy of Sciences |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/52506]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Li, Guosheng |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing, Peoples R China 2.Jiangxi Normal Univ, Key Lab Poyang Lake Wetland & Watershed Res, Minist Educ, Nanchang, Jiangxi, Peoples R China 3.Chongqing Inst Green & Intelligent Technol, Chongqing, Peoples R China |
| 推荐引用方式 GB/T 7714 | He, Lei,Chen, Jilong,Zhang, Yue,et al. An EEMD and BP neural network hybrid approach for modeling regional sea level change[J]. DESALINATION AND WATER TREATMENT,2018,121:139-146. |
| APA | He, Lei,Chen, Jilong,Zhang, Yue,Guo, Tengjiao,&Li, Guosheng.(2018).An EEMD and BP neural network hybrid approach for modeling regional sea level change.DESALINATION AND WATER TREATMENT,121,139-146. |
| MLA | He, Lei,et al."An EEMD and BP neural network hybrid approach for modeling regional sea level change".DESALINATION AND WATER TREATMENT 121(2018):139-146. |
入库方式： OAI收割
来源：地理科学与资源研究所
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