A Review of Application of Machine Learning in Storm Surge Problems
文献类型：期刊论文
| 作者 | Qin, Yue1,2; Su, Changyu3,4; Chu, Dongdong5; Zhang, Jicai6; Song, Jinbao1 |
| 刊名 | JOURNAL OF MARINE SCIENCE AND ENGINEERING
 |
| 出版日期 | 2023-09-01 |
| 卷号 | 11期号:9页码:35 |
| 关键词 | storm surge prediction machine learning hybrid methods physics-informed neural networks |
| DOI | 10.3390/jmse11091729 |
| 通讯作者 | Chu, Dongdong(chu_d0907@foxmail.com) ; Zhang, Jicai(jicai_zhang@163.com) |
| 英文摘要 | The rise of machine learning (ML) has significantly advanced the field of coastal oceanography. This review aims to examine the existing deficiencies in numerical predictions of storm surges and the effort that has been made to improve the predictive accuracy through the application of ML. The readers are guided through the steps required to implement ML algorithms, from the first step of formulating problems to data collection and determination of input features to model selection, development and evaluation. Additionally, the review explores the application of hybrid methods, which combine the bilateral advantages of data-driven methods and physics-based models. Furthermore, the strengths and limitations of ML methods in predicting storm surges are thoroughly discussed, and research gaps are identified. Finally, we outline a vision toward a trustworthy and reliable storm surge forecasting system by introducing novel physics-informed ML techniques. We are meant to provide a primer for beginners and experts in coastal ocean sciences who share a keen interest in ML methodologies in the context of storm surge problems. |
| WOS关键词 | ARTIFICIAL NEURAL-NETWORK ; ENSEMBLE SEA-LEVEL ; TROPICAL CYCLONE ; PREDICTION ; MODEL ; FORECAST ; INTELLIGENCE ; WEATHER ; IMPACT ; INUNDATION |
| WOS研究方向 | Engineering ; Oceanography |
| 语种 | 英语 |
| WOS记录号 | WOS:001071929800001 |
| 资助机构 | The storm surge observations were provided by the Zhejiang Province Ocean and Fisheries Bureau. The typhoon data was provided by the International Best Track Archive for Climate Stewardship (IBTrACS) and the China Meteorological Administration (CMA). We th ; Zhejiang Province Ocean and Fisheries Bureau ; China Meteorological Administration (CMA) |
| 源URL | [http://ir.yic.ac.cn/handle/133337/36734]  |
| 专题 | 烟台海岸带研究所_中科院烟台海岸带研究所知识产出 |
| 通讯作者 | Chu, Dongdong; Zhang, Jicai |
| 作者单位 | 1.Zhejiang Univ, Inst Phys Oceanog & Remote Sensing, Ocean Coll, Zhoushan 316000, Peoples R China 2.Minist Nat Resources, Key Lab Marine Environm Survey Technol & Applicat, Guangzhou 510300, Peoples R China 3.Chinese Acad Sci, Yantai Inst Coastal Zone Res, CAS Key Lab Coastal Environm Proc & Ecol Remediat, Yantai 264003, Peoples R China 4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 5.Changjiang River Sci Res Inst, River Res Dept, Wuhan 430010, Peoples R China 6.East China Normal Univ, State Key Lab Estuarine & Coastal Res, Shanghai 200241, Peoples R China |
| 推荐引用方式 GB/T 7714 | Qin, Yue,Su, Changyu,Chu, Dongdong,et al. A Review of Application of Machine Learning in Storm Surge Problems[J]. JOURNAL OF MARINE SCIENCE AND ENGINEERING,2023,11(9):35. |
| APA | Qin, Yue,Su, Changyu,Chu, Dongdong,Zhang, Jicai,&Song, Jinbao.(2023).A Review of Application of Machine Learning in Storm Surge Problems.JOURNAL OF MARINE SCIENCE AND ENGINEERING,11(9),35. |
| MLA | Qin, Yue,et al."A Review of Application of Machine Learning in Storm Surge Problems".JOURNAL OF MARINE SCIENCE AND ENGINEERING 11.9(2023):35. |
入库方式： OAI收割
来源：烟台海岸带研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。