Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography (한국측량학회지)

Korean Society of Surveying, Geodesy, Photogrammetry and Cartography (한국측량학회)
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Shelter location-allocation for Tsunami Using Floating Population and Genetic Algorithm

유동인구 데이터와 유전자 알고리즘을 이용한 지진해일 대피소 선정

  • Bae, Junsu (School of Civil and Environmental Engineering, Yonsei University) ;
  • Kim, Mi-Kyeong (Agency for Defense Development) ;
  • Yoo, Suhong (School of Civil and Environmental Engineering, Yonsei University) ;
  • Heo, Joon (School of Civil and Environmental Engineering, Yonsei University) ;
  • Sohn, Hong-Gyoo (School of Civil and Environmental Engineering, Yonsei University)
  • Received : 2019.05.29
  • Accepted : 2019.06.18
  • Published : 2019.06.30
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Abstract

Recently, large and small earthquakes have occurred in the Korean peninsula. In this sense, Korea is no longer considered as an earthquake free zone. Especially, it is necessary to respond quickly to earthquake tsunami which may be caused by the influence of neighboring countries with large earthquakes. Since the occurrence of tsunamis can cause great casualties, it is very important to allocate the location of the shelter in case of an earthquake. Although many researches on shelter allocation have been conducted in various ways, but most of them have been analyzed based on administrative district resident data, resulting in a lack of reality. In this study, floating population data were used to reflect reality in case of emergency situations, and genetic algorithm, which produce good results among the heuristic algorithms, was used to select shelter locations. The number of evacuees was used as a objective function of genetic algorithm and the optimal solution was found through selection, crossover and mutation. As a result of the research on Busan Haeundae-Gu, selected as a research area, allocating eight shelters was the most efficient. The location of the new shelters was selected not only in residential areas but also in major tourist areas whose results can not be derived from administrative district resident data alone, and the importance of utilizing the floating population data was confirmed through this study.

최근 한반도에서도 크고 작은 지진이 발생하여 더 이상 한국은 지진 안전지대로 볼 수 없으며, 특히 큰 규모의 지진 발생이 잦은 주변국의 영향으로 인해 발생할 수 있는 지진해일에 대한 신속한 대응이 필요하다. 지진해일의 발생은 큰 인명피해를 초래할 수 있으므로 지진 발생에 대비하여 대피소의 위치를 선정하는 것은 매우 중요한 일이다. 기존에 대피소 관련 연구가 다양하게 진행되었지만, 사용한 자료는 대부분 대피소 주변의 정적인 정주인구를 바탕으로 분석되어 현실성이 결여되어있다. 본 연구에서는 긴급상황 발생 시 현실성을 반영하기 위해 유동인구 데이터를 사용하였고, 대피소 위치선정에 다수 활용되고 휴리스틱 알고리즘 중 좋은 결과를 도출해내는 유전자 알고리즘을 이용하였다. 선택, 교차, 변이 과정을 통해 대피 가능 인원을 유전자 알고리즘의 목적함수로 사용하여 최적지를 탐색하였으며, 연구지역으로 선정한 부산 해운대구를 중심으로 연구한 결과, 총 8개의 대피소를 설치하는 것이 가장 효율성이 높은 것으로 최종 도출되었다. 최종 선정된 대피소의 위치는 일반거주지역뿐만 아니라 주요 관광지 주변도 선정되었는데, 이는 정주인구 통계자료만으로는 도출될 수 없는 결과로 본 연구를 통해 유동인구 데이터 활용의 중요성을 확인하였다.

Keywords

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Fig. 1. Study Area

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Fig. 2. Floating population in study area; (a) monthly, (b) by time

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Fig. 3. Example of shortest path between centroid of candidate shelter and floating population

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Fig. 4. Evacuation distance from the centroid of the administrative district

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Fig. 5. Flowchart of genetic algorithm

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Fig. 6. Structure of chromosome

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Fig. 8. Experiment result for the parameter; (a) Number of generation, (b) Crossover probability, (c) Mutation probability, (d) Selection rate

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Fig. 9. Distribution of floating population

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Fig. 10. The number of evacuees according to the number of shelters

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Fig. 11. The location of new eight shelter

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Fig. 12. The location of the new shelter and the distribution of the floating population that can be moved to the new shelter in time; the number of new shelter (a) one (b) two, (c) three, (d) four, (e) five, (f) six, (g) seven, (h) eight

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Fig. 13. Location of a resident area, tourist spot and New shelter

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Fig. 7. (a) Example of onepoint crossover, (b) example of mutation

Table 1. Parameter Setting for Genetic Algorithm

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