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JDR Vol.13 No.1 pp. 50-61
(2018)
doi: 10.20965/jdr.2018.p0050

Paper:

Land Cover Change Simulations in Yangon Under Several Scenarios of Flood and Earthquake Vulnerabilities with Master Plan

Tanakorn Sritarapipat and Wataru Takeuchi

The University of Tokyo
6-1 Komaba 4-chome, Meguro, Tokyo 153-8505, Japan

Corresponding author

Received:
September 1, 2017
Accepted:
February 5, 2018
Published:
February 20, 2018
Keywords:
prediction of urban expansion, disaster risk assessment, disaster risk reduction, Landsat
Abstract

Yangon is the largest city and major economic area in Myanmar. However, it is considered to have a high risk of floods and earthquakes. In order to mitigate future flood and earthquake damage in Yangon, land cover change simulations considering flood and earthquake vulnerabilities are needed to support urban planning and management. This paper proposes land cover change simulations in Yangon from 2020 to 2040 under various scenarios of flood and earthquake vulnerabilities with a master plan. In our methodology, we used a dynamic statistical model to predict urban expansion in Yangon from 2020 to 2040. We employed a master plan as the future dataset to enhance the prediction of urban expansion. We applied flood and earthquake vulnerabilities based on multi-criteria analysis as the areas vulnerable to disaster. We simulated land cover changes from 2020 to 2040 considering the vulnerable areas with a master plan for multiple scenarios. The experiments indicated that by using a master plan, some of the predicted urban areas are still located in areas highly vulnerable to floods and earthquakes. By integrating the prediction of urban expansion with flood and earthquake vulnerabilities, the predicted urban areas can effectively avoid areas highly vulnerable to floods and earthquakes.

Cite this article as:
T. Sritarapipat and W. Takeuchi, “Land Cover Change Simulations in Yangon Under Several Scenarios of Flood and Earthquake Vulnerabilities with Master Plan,” J. Disaster Res., Vol.13 No.1, pp. 50-61, 2018.
Data files:
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