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An EEMD and BP neural network hybrid approach for modeling regional sea level change
He, Lei1,2; Chen, Jilong3; Zhang, Yue2; Guo, Tengjiao2; Li, Guosheng2
2018-07-01
Source PublicationDESALINATION AND WATER TREATMENT
ISSN1944-3994
Volume121Pages:139-146
Corresponding AuthorLi, Guosheng(ligs@igsnrr.ac.cn)
AbstractSea 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.
KeywordRegional variations Sea level oscillations Pearl River Delta
DOI10.5004/dwt.2018.22378
WOS KeywordTIME-SERIES ; CHANGE SCENARIOS ; COASTAL ZONES ; TIDE GAUGES ; RISE ; DECOMPOSITION ; VARIABILITY ; ALTIMETRY ; VULNERABILITY ; PREDICTION
Indexed BySCI
Language英语
Funding ProjectNational 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]
Funding OrganizationNational 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
WOS Research AreaEngineering ; Water Resources
WOS SubjectEngineering, Chemical ; Water Resources
WOS IDWOS:000446586600019
PublisherDESALINATION PUBL
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/52507
Collection中国科学院地理科学与资源研究所
Corresponding AuthorLi, Guosheng
Affiliation1.Jiangxi Normal Univ, Key Lab Poyang Lake Wetland & Watershed Res, Minist Educ, Nanchang, Jiangxi, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing, Peoples R China
3.Chongqing Inst Green & Intelligent Technol, Chongqing, Peoples R China
Recommended Citation
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.
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