Sea-water-level prediction via combined wavelet decomposition, neuro-fuzzy and neural networks using SLA and wind information

doi:10.1007/s13131-020-1569-1

	Sea-water-level prediction via combined wavelet decomposition, neuro-fuzzy and neural networks using SLA and wind information
	Wang, Bao 1,2; Wang, Bin 2; Wu, Wenzhou 3,4; Xi, Changbai 1; Wang, Jiechen 1,5,6
刊名	ACTA OCEANOLOGICA SINICA
	2020-05-01
卷号	39 期号:5 页码:157-167
关键词	sea-water level prediction ANFIS wavelet decomposition wind
ISSN号	0253-505X
DOI	10.1007/s13131-020-1569-1
通讯作者	Wang, Jiechen(wangjiechen@nju.edu.cn)
英文摘要	Sea-water-level (SWL) prediction significantly impacts human lives and maritime activities in coastal regions, particularly at offshore locations with shallow water levels. Long-term SWL forecasts, which are conventionally obtained via harmonic analysis, become ineffective when nonperiodic meteorological events predominate. Artificial intelligence combined with other data-processing methods can effectively forecast highly nonlinear and nonstationary inflow patterns by recognizing historical relationships between input and output. These techniques are considerably useful in time-series data predictions. This paper reports the development of a hybrid model to realize accurate multihour SWL forecasting by combining an adaptive neuro-fuzzy inference system (ANFIS) with wavelet decomposition while using sea-level anomaly (SLA) and wind-shear-velocity components as inputs. Numerous wavelet-ANFIS (WANFIS) models have been tested using different inputs to assess their applicability as alternatives to the artificial neural network (ANN), wavelet ANN (WANN), and ANFIS models. Different error definitions have been used to evaluate results, which indicate that integrated wavelet-decomposition and ANFIS models improve the accuracy of SWL prediction and that the inputs of SLA and wind-shear velocity exhibit superior prediction capability compared to conventional SWL-only models.
资助项目	National Key R&D Program of China[2016YFC1402609]
WOS关键词	HARMONIC-ANALYSIS ; INFERENCE SYSTEM ; STORM-SURGE ; MODEL ; METHODOLOGY ; MACHINE ; TAIWAN ; HARBOR ; TIDES ; COAST
WOS研究方向	Oceanography
语种	英语
出版者	SPRINGER
WOS记录号	WOS:000540198800017
资助机构	National Key R&D Program of China
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/162254]
专题	中国科学院地理科学与资源研究所
通讯作者	Wang, Jiechen
作者单位	1.Nanjing Univ, Sch Geog & Ocean Sci, Nanjing 210023, Peoples R China 2.Natl Marine Environm Forecasting Ctr, Beijing 100081, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 5.Nanjing Univ, Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China 6.Nanjing Univ, Jiangsu Prov Key Lab Geog Informat Sci & Technol, Nanjing 210023, Peoples R China
推荐引用方式 GB/T 7714	Wang, Bao,Wang, Bin,Wu, Wenzhou,et al. Sea-water-level prediction via combined wavelet decomposition, neuro-fuzzy and neural networks using SLA and wind information[J]. ACTA OCEANOLOGICA SINICA,2020,39(5):157-167.
APA	Wang, Bao,Wang, Bin,Wu, Wenzhou,Xi, Changbai,&Wang, Jiechen.(2020).Sea-water-level prediction via combined wavelet decomposition, neuro-fuzzy and neural networks using SLA and wind information.ACTA OCEANOLOGICA SINICA,39(5),157-167.
MLA	Wang, Bao,et al."Sea-water-level prediction via combined wavelet decomposition, neuro-fuzzy and neural networks using SLA and wind information".ACTA OCEANOLOGICA SINICA 39.5(2020):157-167.