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Physics-Guided Deep Learning Model for Ocean Waves and Storm Surges Induced by Tropical Cyclones with Unified Underlying Logic

文献类型：期刊论文

作者Li, Ao5,6; Qian, Cheng-cheng7; Jiang, Hao-yu4,6; Feng, Xing-ru3; Lu, Wen-fang2; Mu, Lin4; Dong, Chang-ming1
刊名CHINA OCEAN ENGINEERING
出版日期2026-02-01
卷号40期号:1页码:132-143
关键词physics-guided deep learning ocean waves storm surge
ISSN号0890-5487
DOI10.1007/s13344-026-0010-8
通讯作者Jiang, Hao-yu(Haoyujiang@szu.edu.cn) ; Dong, Chang-ming(cmdong@nuist.edu.cn)
英文摘要Accurate and efficient prediction of ocean waves and storm surges induced by tropical cyclones is essential for coastal hazard mitigation and maritime safety. While traditional numerical models are reliable, they require substantial computational resources. Deep learning (DL) offers an alternative approach for modeling these phenomena. Although existing DL models typically address ocean waves or storm surges separately, both phenomena represent responses to atmospheric forcing, suggesting that they can be modeled using identical underlying principles within a data-driven framework. This study, guided by the physics of ocean wave and storm surge generation and evolution, presents a unified DL model that simultaneously predicts both phenomena using current and historical wind and sea level pressure field data. The model effectively captures the complex nonlinear relationships between meteorological inputs and hydrodynamic outputs with high accuracy. Results demonstrate that the proposed model accurately predicts both significant wave height (SWH) and storm surge height, showing strong correlation with numerical models and validating our hypothesis that these phenomena can be modeled collectively using identical input parameters. The model demonstrates superior computational efficiency compared with traditional numerical models while maintaining high accuracy, making it particularly suitable for real-time operational forecasting and climate research of ocean waves and storm surges.
资助项目National Key Research and Development Program of China[2023YFC3008203] ; Shenzhen Science and Technology Program[KCXFZ20240903093100001] ; Guangdong Basic and Applied Basic Research Foundation[2024A1515012032] ; Guangdong Talent Program[2021CX02H070] ; Key Project of the Department of Education of Hubei Province[202538203] ; Research and Applications of Marine environmental Security Technology in Specific Scenarios[2023B17-YJC] ; Research and Applications of Marine environmental Security Technology in Specific Scenarios[2023B12-YJC] ; Laoshan Laboratory
WOS研究方向Engineering ; Water Resources
语种英语
WOS记录号WOS:001693256700013
出版者SPRINGER MEDIZIN VERLAG GmBH
源URL[http://ir.qdio.ac.cn/handle/337002/204772]  
专题海洋研究所_海洋环流与波动重点实验室
通讯作者Jiang, Hao-yu; Dong, Chang-ming
作者单位1.Nanjing Univ Informat Sci & Technol, Sch Marine Sci, Nanjing 210044, Peoples R China
2.Sun Yat Sen Univ, Sch Marine Sci, Zhuhai 519082, Peoples R China
3.Chinese Acad Sci, CAS Key Lab Ocean Circulat & Waves, Inst Oceanol, Qingdao 266071, Peoples R China
4.Shenzhen Univ, Coll Life Sci & Oceanog, Shenzhen 518055, Peoples R China
5.China Univ Geosci, Coll Marine Sci & Technol, Wuhan 430074, Peoples R China
6.Qingdao Marine Sci & Technol Ctr, Lab Reg Oceanog & Numer Modeling, Qingdao 266061, Peoples R China
7.North China Sea Marine Forecasting & Hazard Mitiga, Qingdao 266061, Peoples R China
推荐引用方式
GB/T 7714		 Li, Ao,Qian, Cheng-cheng,Jiang, Hao-yu,et al. Physics-Guided Deep Learning Model for Ocean Waves and Storm Surges Induced by Tropical Cyclones with Unified Underlying Logic[J]. CHINA OCEAN ENGINEERING,2026,40(1):132-143.
APA Li, Ao.,Qian, Cheng-cheng.,Jiang, Hao-yu.,Feng, Xing-ru.,Lu, Wen-fang.,...&Dong, Chang-ming.(2026).Physics-Guided Deep Learning Model for Ocean Waves and Storm Surges Induced by Tropical Cyclones with Unified Underlying Logic.CHINA OCEAN ENGINEERING,40(1),132-143.
MLA Li, Ao,et al."Physics-Guided Deep Learning Model for Ocean Waves and Storm Surges Induced by Tropical Cyclones with Unified Underlying Logic".CHINA OCEAN ENGINEERING 40.1(2026):132-143.

入库方式： OAI收割

来源：海洋研究所

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