Articles | Volume 24, issue 6
https://doi.org/10.5194/nhess-24-2003-2024
https://doi.org/10.5194/nhess-24-2003-2024
Research article
 | 
14 Jun 2024
Research article |  | 14 Jun 2024

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, Hao Qin, Shining Huang, Wei Wei, Haoyu Jiang, and Lin Mu

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Cited articles

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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.
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