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Automatic Type Recognition and Mapping of Global Tropical Cyclone Disaster Chains (TDC)

Author

Listed:
  • Ran Wang

    (School of Geography, Beijing Normal University, Beijing 100875, China
    Laboratory of Regional Geography, Beijing Normal University, Beijing 100875, China)

  • Laiyin Zhu

    (Department of Geography, Western Michigan University, 1903 W Michigan Ave, Kalamazoo, MI 49008-5424, USA)

  • Han Yu

    (School of Geography, Beijing Normal University, Beijing 100875, China
    Laboratory of Regional Geography, Beijing Normal University, Beijing 100875, China
    School of Agriculture & Forestry Economics and Management, Lanzhou University of Finance and Economics, Lanzhou 730101, China)

  • Shujuan Cui

    (School of Geography, Beijing Normal University, Beijing 100875, China
    Laboratory of Regional Geography, Beijing Normal University, Beijing 100875, China)

  • Jing’ai Wang

    (School of Geography, Beijing Normal University, Beijing 100875, China
    Laboratory of Regional Geography, Beijing Normal University, Beijing 100875, China)

Abstract

The catastrophic events caused by meteorological disasters are becoming more severe in the context of global warming. The disaster chains triggered by Tropical Cyclones induce the serious losses of population and economy. It is necessary to make the regional type recognition of Tropical Cyclone Disaster Chain (TDC) effective in order to make targeted preventions. This study mainly explores the method of automatic recognition and the mapping of TDC and designs a software system. We constructed an automatic recognition system in terms of the characteristics of a hazard-formative environment based on the theory of a natural disaster system. The ArcEngine components enable an intelligent software system to present results by the automatic mapping approach. The study data comes from global metadata such as Digital Elevation Model (DEM), terrain slope, population density and Gross Domestic Product (GDP). The result shows that: (1) according to the characteristic of geomorphology type, we establish a type of recognition system for global TDC; (2) based on the recognition principle, we design a software system with the functions of automatic recognition and mapping; and (3) we validate the type of distribution in terms of real cases of TDC. The result shows that the automatic recognition function has good reliability. The study can provide the basis for targeted regional disaster prevention strategy, as well as regional sustainable development.

Suggested Citation

  • Ran Wang & Laiyin Zhu & Han Yu & Shujuan Cui & Jing’ai Wang, 2016. "Automatic Type Recognition and Mapping of Global Tropical Cyclone Disaster Chains (TDC)," Sustainability, MDPI, vol. 8(10), pages 1-16, October.
    • Handle: RePEc:gam:jsusta:v:8:y:2016:i:10:p:1066-:d:81137
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    References listed on IDEAS

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