Multi-step ahead short-term predictions of storm surge level using CNN and LSTM network
文献类型：期刊论文
| 作者 | Wang, Bao3,4,5,6; Liu, Shichao6; Wang, Bin6; Wu, Wenzhou1; Wang, Jiechen3,4,5,7; Shen, Dingtao2 |
| 刊名 | ACTA OCEANOLOGICA SINICA
 |
| 出版日期 | 2021-11-01 |
| 卷号 | 40期号:11页码:104-118 |
| 关键词 | storm surge prediction CNN LSTM combination |
| ISSN号 | 0253-505X |
| DOI | 10.1007/s13131-021-1763-9 |
| 通讯作者 | Shen, Dingtao(dingtaoshen@outlook.com) |
| 英文摘要 | Storm surges pose significant danger and havoc to the coastal residents' safety, property, and lives, particularly at offshore locations with shallow water levels. Predictions of storm surges with hours of warning time are important for evacuation measures in low-lying regions and coastal management plans. In addition to experienced predictions and numerical models, artificial intelligence (AI) techniques are also being used widely for short-term storm surge prediction owing to their merits in good level of prediction accuracy and rapid computations. Convolutional neural network (CNN) and long short-term memory (LSTM) are two of the most important models among AI techniques. However, they have been scarcely utilised for surge level (SL) forecasting, and combinations of the two models are even rarer. This study applied CNN and LSTM both individually and in combination towards multi-step ahead short-term storm surge level prediction using observed SL and wind information. The architectures of the CNN, LSTM, and two sequential techniques of combining the models (LSTM-CNN and CNN-LSTM) were constructed via a trial-and-error approach and knowledge obtained from previous studies. As a case study, 11 a of hourly observed SL and wind data of the Xiuying Station, Hainan Province, China, were organised as inputs for training to verify the feasibility and superiority of the proposed models. The results show that CNN and LSTM had evident advantages over support vector regression (SVR) and multilayer perceptron (MLP), and the combined models outperformed the individual models (CNN and LSTM), mostly by 4%-6%. However, on comparing the model computed predictions during two severe typhoons that resulted in extreme storm surges, the accuracy was found to improve by over 10% at all forecasting steps. |
| WOS关键词 | ARTIFICIAL NEURAL-NETWORK ; HARMONIC-ANALYSIS ; TIME PREDICTION ; SEA-LEVEL ; MODEL ; FUZZY ; DECOMPOSITION ; COMBINATION ; FORECAST ; HARBOR |
| 资助项目 | National Key Research and Development Program of China[2016YFC1402609] ; Open Fund of the Key Laboratory of Marine Hazards Forecasting, Ministry of Natural Resources[LOMF 1804] ; National Natural Science Foundation of China[42077438] |
| WOS研究方向 | Oceanography |
| 语种 | 英语 |
| WOS记录号 | WOS:000738675400010 |
| 出版者 | SPRINGER |
| 资助机构 | National Key Research and Development Program of China ; Open Fund of the Key Laboratory of Marine Hazards Forecasting, Ministry of Natural Resources ; National Natural Science Foundation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/169473]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Shen, Dingtao |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 2.Changjiang Water Resources Commiss, Changjiang River Sci Res Inst, Wuhan 430010, Peoples R China 3.Nanjing Univ, Jiangsu Prov Key Lab Geog Informat Sci & Technol, Nanjing 210023, Peoples R China 4.Nanjing Univ, Key Lab Land Satellite Remote Sensing Applicat, Minist Nat Resources, Nanjing 210023, Peoples R China 5.Nanjing Univ, Sch Geog & Ocean Sci, Nanjing 210023, Peoples R China 6.Minist Nat Resources, Key Lab Marine Hazards Forecasting, Natl Marine Environm Forecasting Ctr, Beijing 100081, Peoples R China 7.Nanjing Univ, Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Bao,Liu, Shichao,Wang, Bin,et al. Multi-step ahead short-term predictions of storm surge level using CNN and LSTM network[J]. ACTA OCEANOLOGICA SINICA,2021,40(11):104-118. |
| APA | Wang, Bao,Liu, Shichao,Wang, Bin,Wu, Wenzhou,Wang, Jiechen,&Shen, Dingtao.(2021).Multi-step ahead short-term predictions of storm surge level using CNN and LSTM network.ACTA OCEANOLOGICA SINICA,40(11),104-118. |
| MLA | Wang, Bao,et al."Multi-step ahead short-term predictions of storm surge level using CNN and LSTM network".ACTA OCEANOLOGICA SINICA 40.11(2021):104-118. |
入库方式： OAI收割
来源：地理科学与资源研究所
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