Preprints
https://doi.org/10.5194/nhess-2023-199
https://doi.org/10.5194/nhess-2023-199
25 Jan 2024
 | 25 Jan 2024
Status: a revised version of this preprint was accepted for the journal NHESS and is expected to appear here in due course.

Quantitative Study of Storm Surge Risk Assessment in Undeveloped Coastal Area of China Based on Deep Learning and Geographic Information System (GIS) Techniques: A Case Study of Double-Moon Bay Zone

Lichen Yu, Shining Huang, Hao Qin, Wei Wei, and Lin Mu

Abstract. Storm surge is a common nature disaster in China southern coastal area, which usually causes heavy human life and economic losses. With the economic development and population concentration of coastal cities, the storm surges may result in more impacts and damage in the future. Therefore, it is of vital importance to conduct risk assessment to identify high-risk areas and evaluate economic losses. However, quantitative study of storm surge risk assessment in undeveloped areas of China is difficult, since there is a lack of building characters and damage assessment data. Aiming at the problem of data missing in undeveloped areas of China, this paper proposes a methodology for conducting storm surge risk assessment quantitatively based on deep learning and geographic information system (GIS) techniques. Five defined storm surge inundation scenarios with different typhoon return periods are simulated by coupled FVCOM-SWAN model, the reliability of which is validated using official measurements. Building footprints of the study area are extracted through TransUNet deep learning model and Remote Sensing Image (RSI), while building heights are obtained through Unmanned Aerial Vehicle (UAV) measurement. Subsequently, economic losses are quantitatively calculated by combing the adjusted depth-damage functions and overlay analysis of the buildings exposed to storm surge inundation. Zonation maps of the study area are illustrated to display the risk levels according to the economic losses. The quantitative risk assessment and zonation maps can help the government to make storm surge disaster prevention measures and optimize land use planning, and thus to reduce the potential economic losses of the coastal area.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Lichen Yu, Shining Huang, Hao Qin, Wei Wei, and Lin Mu

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2023-199', Anonymous Referee #1, 07 Mar 2024
    • AC1: 'Reply on RC1', Hao Qin, 15 Mar 2024
  • RC2: 'Comment on nhess-2023-199', Anonymous Referee #2, 09 Mar 2024
    • AC2: 'Reply on RC2', Hao Qin, 15 Mar 2024
      • RC3: 'Reply on AC2', Anonymous Referee #2, 16 Mar 2024
        • AC3: 'Reply on RC3', Hao Qin, 06 Apr 2024

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2023-199', Anonymous Referee #1, 07 Mar 2024
    • AC1: 'Reply on RC1', Hao Qin, 15 Mar 2024
  • RC2: 'Comment on nhess-2023-199', Anonymous Referee #2, 09 Mar 2024
    • AC2: 'Reply on RC2', Hao Qin, 15 Mar 2024
      • RC3: 'Reply on AC2', Anonymous Referee #2, 16 Mar 2024
        • AC3: 'Reply on RC3', Hao Qin, 06 Apr 2024
Lichen Yu, Shining Huang, Hao Qin, Wei Wei, and Lin Mu
Lichen Yu, Shining Huang, Hao Qin, Wei Wei, and Lin Mu

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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 the adjusted depth-damage functions with inundation simulation results. Zonation maps illustrate risk levels based on economic losses, aiding in disaster prevention measures to reduce coastal area losses.
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