A Review of Application of Machine Learning in Storm Surge Problems

doi:10.3390/jmse11091729

	A Review of Application of Machine Learning in Storm Surge Problems
	Qin, Yue 1,2; Su, Changyu 3,4; Chu, Dongdong 5; Zhang, Jicai 6; Song, Jinbao 1
发表期刊	JOURNAL OF MARINE SCIENCE AND ENGINEERING
	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.
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)
收录类别	SCI
语种	英语
关键词[WOS]	ARTIFICIAL NEURAL-NETWORK ; ENSEMBLE SEA-LEVEL ; TROPICAL CYCLONE ; PREDICTION ; MODEL ; FORECAST ; INTELLIGENCE ; WEATHER ; IMPACT ; INUNDATION
研究领域[WOS]	Engineering ; Oceanography
WOS记录号	WOS:001071929800001
引用统计	被引频次：2[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.yic.ac.cn/handle/133337/32873
专题	中国科学院海岸带环境过程与生态修复重点实验室
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.