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Sustainability 2016, 8(10), 1066; doi:10.3390/su8101066

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

1,2
,
3
,
1,2,4
,
1,2
and
1,2,*
1
School of Geography, Beijing Normal University, Beijing 100875, China
2
Laboratory of Regional Geography, Beijing Normal University, Beijing 100875, China
3
Department of Geography, Western Michigan University, 1903 W Michigan Ave, Kalamazoo, MI 49008-5424, USA
4
School of Agriculture & Forestry Economics and Management, Lanzhou University of Finance and Economics, Lanzhou 730101, China
*
Author to whom correspondence should be addressed.
Academic Editors: Alexandru Ozunu, Dacinia Crina Petrescu and Marc A. Rosen
Received: 26 July 2016 / Revised: 27 September 2016 / Accepted: 18 October 2016 / Published: 21 October 2016
(This article belongs to the Special Issue Resilience to Natural and Man-Made Disasters)
View Full-Text   |   Download PDF [1818 KB, uploaded 21 October 2016]   |  

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. View Full-Text
Keywords: Tropical Cyclone Disaster Chain (TDC); global; automatic recognition; automatic mapping Tropical Cyclone Disaster Chain (TDC); global; automatic recognition; automatic mapping
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Wang, R.; Zhu, L.; Yu, H.; Cui, S.; Wang, J. Automatic Type Recognition and Mapping of Global Tropical Cyclone Disaster Chains (TDC). Sustainability 2016, 8, 1066.

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