Articles | Volume 24, issue 6
https://doi.org/10.5194/nhess-24-2003-2024
© Author(s) 2024. 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-24-2003-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Quantitative study of storm surge risk assessment in an undeveloped coastal area of China based on deep learning and geographic information system techniques: a case study of Double Moon Bay
Lichen Yu
Hubei Key Laboratory of Marine Geological Resources, College of Marine Science and Technology, China University of Geosciences, Wuhan, 430074, China
Shenzhen Research Institute, China University of Geosciences, Shenzhen, 518057, China
Hao Qin
CORRESPONDING AUTHOR
Hubei Key Laboratory of Marine Geological Resources, College of Marine Science and Technology, China University of Geosciences, Wuhan, 430074, China
Shenzhen Research Institute, China University of Geosciences, Shenzhen, 518057, China
Shining Huang
Marine Information Center, Department of Natural Resources of Huizhou Bureau, Huizhou, 516003, China
Wei Wei
Hubei Key Laboratory of Marine Geological Resources, College of Marine Science and Technology, China University of Geosciences, Wuhan, 430074, China
Shenzhen Research Institute, China University of Geosciences, Shenzhen, 518057, China
Haoyu Jiang
Hubei Key Laboratory of Marine Geological Resources, College of Marine Science and Technology, China University of Geosciences, Wuhan, 430074, China
Shenzhen Research Institute, China University of Geosciences, Shenzhen, 518057, China
College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, 518060, China
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Storm surges pose a significant flooding risk to coastal areas. This research, taking China's Daya Bay Petrochemical Industrial Zone as a case study, addresses the dynamic nature of flooding events and the limitations of traditional evacuation plans for individuals with restricted real-time information. By combining the hydrological model and artificial intelligence, the method proves highly effective in optimizing evacuation routes, providing invaluable guidance during actual storm surges.
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Sea surface wind and waves are important ocean parameters that can be continuously observed by meteorological buoys. Meteorological buoys are sparse in the ocean due to their high cost of deployment and maintenance. In contrast, low-cost compact wave buoys are suited for deployment in large numbers. Although wave buoys are not designed for wind measurement, we found that deep learning can estimate wind from wave measurements accurately, making wave buoys a good-quality data source for sea wind.
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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
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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
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.
This paper proposes a quantitative storm surge risk assessment method for data-deficient...
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