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首页> 外文期刊>International Journal of Disaster Risk Science >Relationships Between Evacuation Population Size, Earthquake Emergency Shelter Capacity, and Evacuation Time
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Relationships Between Evacuation Population Size, Earthquake Emergency Shelter Capacity, and Evacuation Time

机译:疏散人口规模,地震应急避难所容量和疏散时间之间的关系

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摘要

Abstract Determining the location of earthquake emergency shelters and the allocation of affected population to them are key issues that face shelter planning and emergency management. To solve this emergency shelter location–allocation problem, evacuation time and the construction cost of shelters—both influenced by the evacuation population size and its spatial distribution—are two important considerations. In this article, a mathematical model with two objectives—to minimize total weighted evacuation time (TWET) and total shelter area (TSA)—is allied with a modified particle swarm optimization algorithm to address the problem. The relationships between evacuation population size, evacuation time, and total shelter area are examined using Jinzhan Town in Chaoyang District of Beijing, China, as a case study. The results show that TWET has a power function relationship with TSA under different population size scenarios, and a linear function applies between evacuation population and TWET under different TSAs. The joint relationships of TSA, TWET, and population size show that TWET increases with population increase and TSA decrease, and compared with TSA, population influences TWET more strongly. Given a reliable projection of population change and spatial planning of a study area, this method can be useful for government decision making on the location of earthquake emergency shelters and on the allocation of evacuees to those shelters.
机译:摘要确定地震应急避难所的位置和受影响人口的分配是避难所规划和应急管理面临的关键问题。为了解决这一紧急避难所的位置分配问题,避难时间和避难所的建设成本(均受避难人口规模及其空间分布的影响)是两个重要的考虑因素。在本文中,一个具有两个目标的数学模型-最小化总加权疏散时间(TWET)和总庇护区(TSA)-与改进的粒子群优化算法结合在一起解决了该问题。以北京朝阳区金站镇为例,研究了疏散人口规模,疏散时间与总避难所面积之间的关系。结果表明,在不同人口规模的情景下,TWET与TSA具有幂函数关系,而在不同TSA下,疏散人口与TWET之间存在线性函数关系。 TSA,TWET和人口规模的共同关系表明,TWET随着人口的增加和TSA的减少而增加,并且与TSA相比,人口对TWET的影响更大。给定人口变化的可靠预测和研究区域的空间规划,此方法对于政府在地震应急避难所的位置以及对这些避难所的撤离人员的分配方面的决策很有用。

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