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Method to determine the locations of tsunami vertical evacuation shelters

Author

Listed:
  • Sangki Park
  • John van de Lindt
  • Rakesh Gupta
  • Daniel Cox

Abstract

The 2004 Indian Ocean tsunami and the 2011 Great Tohoku Japan earthquake and tsunami focused a great deal of the world’s attention on the effect of tsunamis on buildings and infrastructure. When a tsunami impacts structures in a coastal community, the structures are often not strong enough to withstand the forces and may collapse. Therefore, to maximize the survival probability, people evacuate to higher ground or move outside the inundation zone. However, this is not always possible because of short warning times for near-field tsunamis. Thus, sheltering-in-place or “sheltering-near-place” using vertical evacuation should be considered as an alternative approach to lateral evacuation from a tsunami inundation zone. This paper presents the method and results of a study to develop and demonstrate a methodology that applied genetic optimization to determine optimal tsunami shelter locations with the goal of reducing evacuation time, thereby maximizing the probability of survival for the population in a coastal community. The City of Cannon Beach, Oregon, USA, was used as an illustrative example. Several cases were investigated ranging from a single shelter to multiple shelters with locations of high elevation already in place near the city. The method can provide decision-support for the determination of locations for tsunami vertical evacuation shelters. The optimum location of the shelter(s), which was found to vary depending on the number of shelters considered, can reduce the evacuation time significantly, thereby reducing the number of fatalities and increasing the safety of a community. Copyright Springer Science+Business Media B.V. 2012

Suggested Citation

  • Sangki Park & John van de Lindt & Rakesh Gupta & Daniel Cox, 2012. "Method to determine the locations of tsunami vertical evacuation shelters," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 63(2), pages 891-908, September.
    • Handle: RePEc:spr:nathaz:v:63:y:2012:i:2:p:891-908
    DOI: 10.1007/s11069-012-0196-3
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    References listed on IDEAS

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    1. Michael Lindell & Jung Kang & Carla Prater, 2011. "The logistics of household hurricane evacuation," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 58(3), pages 1093-1109, September.
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    1. Alireza Mostafizi & Haizhong Wang & Dan Cox & Lori A. Cramer & Shangjia Dong, 2017. "Agent-based tsunami evacuation modeling of unplanned network disruptions for evidence-driven resource allocation and retrofitting strategies," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 88(3), pages 1347-1372, September.
    2. Hisao Nakai & Ryo Horiike & Tomoya Itatani & Yukari Matsumoto, 2022. "Childcare Center Evacuation to Vertical Shelters in a Nankai Trough Tsunami: Models to Predict and Mitigate Risk," Challenges, MDPI, vol. 13(2), pages 1-13, September.
    3. Melissa Gama & Bruno Filipe Santos & Maria Paola Scaparra, 2016. "A multi-period shelter location-allocation model with evacuation orders for flood disasters," EURO Journal on Computational Optimization, Springer;EURO - The Association of European Operational Research Societies, vol. 4(3), pages 299-323, September.
    4. Yash V. Marthak & Eduardo Pérez & Francis A. Méndez Mediavilla, 2021. "A stochastic programming model for tactical product prepositioning at domestic hunger relief organizations impacted by natural hazards," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 107(3), pages 2263-2291, July.
    5. André Trindade & Paula Teves-Costa & Cristina Catita, 2018. "A GIS-based analysis of constraints on pedestrian tsunami evacuation routes: Cascais case study (Portugal)," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 93(1), pages 169-185, September.
    6. Oscar Rodríguez-Espíndola & Juan Gaytán, 2015. "Scenario-based preparedness plan for floods," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 76(2), pages 1241-1262, March.
    7. Haseog Kim & Sangki Park & Hayong Kim, 2016. "The Optimum Production Method for Quality Improvement of Recycled Aggregates Using Sulfuric Acid and the Abrasion Method," IJERPH, MDPI, vol. 13(8), pages 1-14, July.
    8. Nathan Wood & Jeff Peters, 2015. "Variations in population vulnerability to tectonic and landslide-related tsunami hazards in Alaska," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 75(2), pages 1811-1831, January.
    9. Nathan Wood & Jeanne M. Jones & Yoshiki Yamazaki & Kwok-Fai Cheung & Jacinta Brown & Jamie L. Jones & Nina Abdollahian, 2019. "Population vulnerability to tsunami hazards informed by previous and projected disasters: a case study of American Samoa," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 95(3), pages 505-528, February.
    10. Soheyl Khalilpourazari & Seyed Hamid Reza Pasandideh, 2021. "Designing emergency flood evacuation plans using robust optimization and artificial intelligence," Journal of Combinatorial Optimization, Springer, vol. 41(3), pages 640-677, April.

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