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Creation of Tsunami Evacuation Time Map for the Mentawai Islands, Indonesia.

  • Published In: Journal of Earthquake & Tsunami, 2023, v. 17, n. 1. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Ono, Yusuke; Putra, Rusnardi Rahmat 3 of 3

Abstract

The Mentawai Islands are located offshore of West Sumatra, Indonesia, and are likely to be hit by a large-scale tsunami in the future. In this study, maps showing tsunami evacuation times (ETs) along the road on the Mentawai Islands were developed to mitigate tsunami impact. We define the tsunami ET as the time required for an evacuee to move to a safe location through the road network from the tsunami inundation area. The safe locations were determined using a digital elevation model and the assumed tsunami inundation heights. The closest safe place for each location on the road network was determined using the shortest path search algorithm for the graph network. First, areas with high tsunami risk on the islands were identified using the obtained ET maps. Second, the appropriateness of the evacuation approach in the Matobe village on Sipora Island was evaluated based on the ET maps. Finally, population exposures to the different ET levels were estimated to quantify the potential risk of loss of life to a tsunami disaster in the Mentawai Islands. This study contributes to the tsunami disaster preparation of residents of the Mentawai Islands. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Earthquake & Tsunami. 2023/02, Vol. 17, Issue 1, p1
  • Document Type:Article
  • Subject Area:History
  • Publication Date:2023
  • ISSN:1793-4311
  • DOI:10.1142/S1793431122500191
  • Accession Number:162295931
  • Copyright Statement:Copyright of Journal of Earthquake & Tsunami is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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