Articles | Volume 25, issue 12
https://doi.org/10.5194/nhess-25-4767-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-25-4767-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Effective storm surge risk assessment and deep reinforcement learning based evacuation planning: a case study of Daya Bay Petrochemical Industrial Zone
Chuanfeng Liu
State Key Laboratory of Physical Oceanography, Ocean University of China, Qingdao, China
Yan Li
CORRESPONDING AUTHOR
College of Life Science and Oceanography, Shenzhen University, Shenzhen, China
Hao Qin
CORRESPONDING AUTHOR
College of Marine Science and Technology, China University of Geosciences, Wuhan, China
Wenjuan Li
Shenzhen Marine Development and Promotion Center, Shenzhen, China
College of Life Science and Oceanography, Shenzhen University, Shenzhen, China
College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
College of Life Science and Oceanography, Shenzhen University, Shenzhen, China
College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
Darong Liu
College of Marine Science and Technology, China University of Geosciences, Wuhan, China
Kai Zhou
Shenzhen Marine Development and Promotion Center, Shenzhen, China
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Geosci. Model Dev., 18, 5101–5114, https://doi.org/10.5194/gmd-18-5101-2025, https://doi.org/10.5194/gmd-18-5101-2025, 2025
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Large-scale wave modeling is essential for science and society, typically relying on resource-intensive numerical methods to simulate wave dynamics. In this study, we introduce a rolling AI-based method for modeling global significant wave height. Our model achieves accuracy comparable to traditional numerical methods while significantly improving speed, making it operable on standard laptops. This work demonstrates AI's potential to enhance the accuracy and efficiency of global wave modeling.
Lichen Yu, Hao Qin, Shining Huang, Wei Wei, Haoyu Jiang, and Lin Mu
Nat. Hazards Earth Syst. Sci., 24, 2003–2024, https://doi.org/10.5194/nhess-24-2003-2024, https://doi.org/10.5194/nhess-24-2003-2024, 2024
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This paper proposes a quantitative storm surge risk assessment method for data-deficient regions. A coupled model is used to simulate five storm surge scenarios. Deep learning is used to extract building footprints. Economic losses are calculated by combining adjusted depth–damage functions with inundation simulation results. Zoning maps illustrate risk levels based on economic losses, aiding in disaster prevention measures to reduce losses in coastal areas.
Si Wang, Lin Mu, Zhenfeng Yao, Jia Gao, Enjin Zhao, and Lizhe Wang
Nat. Hazards Earth Syst. Sci., 21, 439–462, https://doi.org/10.5194/nhess-21-439-2021, https://doi.org/10.5194/nhess-21-439-2021, 2021
Short summary
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The study provides a comprehensive assessment and zonation of hazard, vulnerability, and risk of storm surge caused by the designed typhoon scenarios in the coastal area of Huizhou. The risk maps can help decision-makers to develop evacuation strategies to minimize civilian casualties. The risk analysis can be utilized to identify risk regions to reduce economic losses. The proposed methodology and procedure can be applied to any coastal city in China for making risk assessments.
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Short summary
Storm surges pose a significant flooding risk to coastal areas. This study presents a comprehensive approach to conduct the storm risk assessment and evacuation route planning in the Daya Bay Petrochemical Industrial Zone. It facilitates a thorough understanding for local government regarding the spatial distribution of road risks and aids residents in swiftly devising optimal evacuation routes, which significantly bolsters efforts in storm surge disaster prevention, mitigation, and contributes.
Storm surges pose a significant flooding risk to coastal areas. This study presents a...
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