Sea-water-level prediction via combined wavelet decomposition, neuro-fuzzy and neural networks using SLA and wind information
文献类型：期刊论文
| 作者 | Wang, Bao3,4; Wang, Bin4; Wu, Wenzhou5,6; Xi, Changbai3; Wang, Jiechen1,2,3 |
| 刊名 | ACTA OCEANOLOGICA SINICA
 |
| 出版日期 | 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. |
| WOS关键词 | HARMONIC-ANALYSIS ; INFERENCE SYSTEM ; STORM-SURGE ; MODEL ; METHODOLOGY ; MACHINE ; TAIWAN ; HARBOR ; TIDES ; COAST |
| 资助项目 | National Key R&D Program of China[2016YFC1402609] |
| WOS研究方向 | Oceanography |
| 语种 | 英语 |
| WOS记录号 | WOS:000540198800017 |
| 出版者 | SPRINGER |
| 资助机构 | National Key R&D Program of China |
| 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, Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China 2.Nanjing Univ, Jiangsu Prov Key Lab Geog Informat Sci & Technol, Nanjing 210023, Peoples R China 3.Nanjing Univ, Sch Geog & Ocean Sci, Nanjing 210023, Peoples R China 4.Natl Marine Environm Forecasting Ctr, Beijing 100081, Peoples R China 5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 6.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, 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. |
入库方式： OAI收割
来源：地理科学与资源研究所
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