亲爱的研友该休息了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!身体可是革命的本钱,早点休息,好梦!

Efficient simulation of natural hazard evacuation for seacoast cities

危害 自然灾害 自然(考古学) 运输工程 环境科学 计算机科学 法律工程学 环境规划 工程类 地理 生物 气象学 生态学 考古
作者
Gabriel Astudillo Muñoz,Verónica Gil-Costa,Mauricio Marín
出处
期刊:International journal of disaster risk reduction [Elsevier]
卷期号:81: 103300-103300
标识
DOI:10.1016/j.ijdrr.2022.103300
摘要

Evacuation plans in seacoast areas are essential for conducting people to secure zones in a timely manner. Typically, evacuation plans are based on the experience of previous evacuation drills, which are expensive processes that require coordination, planning and the collaboration of different institutions and people. During evacuation drills it is difficult to obtain all the data required to analyze the situation and additionally, it is difficult to detect all possible threatening situations. Computer simulations can be used to run evacuation models for evaluating different evacuation scenarios. However, developing realistic simulations is a complex task. Moreover, large simulation models considering many thousands of people demand a high computational cost and thereby, the simulation of different evacuation plans can become a highly time-consuming task. In this work, we present an approach to model and simulate the behavior of people in mass evacuations of seacoast areas. Our proposal aims to improve the computational efficiency of the calculations performed without compromising the quality of results by means of parallel computing. The simulation model divides the geographic area in cells of fixed sizes. Then, to reduce the amount of calculations performed in each simulation timestep, for each simulated agent we compute a mobility model by considering only the agents placed in the closest neighboring cells. The proposed simulation model achieves realistic results by combining geographic data, public census data, the density of the population, the surrounding view of each person and disaggregation by age groups. This reduces the error in decision making and allows a proper estimation of the distance of groups of people that cannot arrive at safe areas. The respective simulator has been implemented using agent-based programming in C++ and OpenMP. The simulation model was evaluated by performing experimentation on actual data collected from the Chilean cities of Iquique and Viña del Mar, and the city of Kesennuma in Japan.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
醉熏的井发布了新的文献求助10
1秒前
4秒前
ls完成签到,获得积分10
5秒前
小手冰冰凉完成签到,获得积分10
5秒前
小九发布了新的文献求助10
6秒前
李健应助醉熏的井采纳,获得10
18秒前
英俊的铭应助醉熏的井采纳,获得10
18秒前
邹家园完成签到 ,获得积分10
18秒前
CodeCraft应助菠萝嘉嘉采纳,获得10
20秒前
20秒前
20秒前
21秒前
Monik完成签到,获得积分10
24秒前
wuwen发布了新的文献求助10
25秒前
zqgxiangbiye发布了新的文献求助50
27秒前
Mia发布了新的文献求助10
27秒前
德文喵发布了新的文献求助10
29秒前
石冠山完成签到,获得积分10
33秒前
852应助susan采纳,获得10
35秒前
36秒前
YDSG完成签到,获得积分10
37秒前
所所应助小九采纳,获得10
39秒前
41秒前
41秒前
科研通AI6.3应助花花懿懿采纳,获得10
42秒前
45秒前
big发布了新的文献求助10
45秒前
小骁同学完成签到,获得积分10
48秒前
asia完成签到 ,获得积分10
49秒前
susan发布了新的文献求助10
50秒前
田様应助小骁同学采纳,获得10
51秒前
Geodada完成签到,获得积分10
59秒前
雨寒完成签到 ,获得积分10
1分钟前
1分钟前
凉宫八月完成签到,获得积分10
1分钟前
诚心爆米花完成签到 ,获得积分10
1分钟前
从容水蓝应助big采纳,获得10
1分钟前
菠萝嘉嘉关注了科研通微信公众号
1分钟前
1分钟前
1分钟前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Modern Epidemiology, Fourth Edition 5000
Kinesiophobia : a new view of chronic pain behavior 5000
Molecular Biology of Cancer: Mechanisms, Targets, and Therapeutics 3000
Digital Twins of Advanced Materials Processing 2000
Propeller Design 2000
Weaponeering, Fourth Edition – Two Volume SET 2000
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 纳米技术 化学工程 生物化学 物理 计算机科学 内科学 复合材料 催化作用 物理化学 光电子学 电极 冶金 细胞生物学 基因
热门帖子
关注 科研通微信公众号,转发送积分 6012362
求助须知:如何正确求助?哪些是违规求助? 7568015
关于积分的说明 16138831
捐赠科研通 5159306
什么是DOI,文献DOI怎么找? 2763030
邀请新用户注册赠送积分活动 1742206
关于科研通互助平台的介绍 1633917