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 BV]
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
充电宝应助开心的易巧采纳,获得10
刚刚
共享精神应助清漪采纳,获得10
刚刚
Hanayu完成签到 ,获得积分0
1秒前
YSE发布了新的文献求助10
2秒前
yuzhongLuo发布了新的文献求助10
2秒前
3秒前
结实觅海完成签到 ,获得积分10
4秒前
鹰酱完成签到,获得积分10
4秒前
evans完成签到,获得积分10
4秒前
上官若男应助Candy采纳,获得10
5秒前
xu发布了新的文献求助50
5秒前
XIANGYI发布了新的文献求助10
7秒前
7秒前
辰12完成签到 ,获得积分10
7秒前
8秒前
11秒前
11秒前
领导范儿应助Snow886采纳,获得10
13秒前
13秒前
云1完成签到,获得积分10
14秒前
加油小李完成签到 ,获得积分10
14秒前
15秒前
科研通AI6.4应助冷傲书萱采纳,获得10
15秒前
Jasper应助冷傲书萱采纳,获得10
15秒前
xuezhao完成签到,获得积分10
16秒前
16秒前
简单妙竹发布了新的文献求助10
16秒前
Zhu发布了新的文献求助10
17秒前
科研通AI6.2应助微笑仰采纳,获得10
18秒前
情怀应助gloval采纳,获得10
18秒前
烟花应助xuezhao采纳,获得10
19秒前
19秒前
21秒前
kevin1018发布了新的文献求助10
23秒前
silly发布了新的文献求助10
24秒前
24秒前
赘婿应助Candy采纳,获得10
24秒前
淡淡萍完成签到,获得积分10
24秒前
Zhu完成签到,获得积分10
24秒前
TT001发布了新的文献求助10
25秒前
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
Lewis’s Child and Adolescent Psychiatry: A Comprehensive Textbook Sixth Edition 2000
Wolffs Headache and Other Head Pain 9th Edition 1000
Continuing Syntax 1000
Encyclopedia of Quaternary Science Reference Work • Third edition • 2025 800
Signals, Systems, and Signal Processing 510
荧光膀胱镜诊治膀胱癌 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6221412
求助须知:如何正确求助?哪些是违规求助? 8046400
关于积分的说明 16774523
捐赠科研通 5306796
什么是DOI,文献DOI怎么找? 2827014
邀请新用户注册赠送积分活动 1805230
关于科研通互助平台的介绍 1664593