气候变化领域集成服务门户(CCPortal): Particle swarm optimization for source localization in realistic complex urban environments

CCPortal

DOI	10.1016/j.atmosenv.2021.118636
	Particle swarm optimization for source localization in realistic complex urban environments
	Gunawardena N.; Leang K.K.; Pardyjak E.
发表日期	2021
ISSN	1352-2310
卷号	262
英文摘要	In this work, we present a method to localize a source in complex urban environments using particle swarm optimization (PSO). Instead of using PSO to minimize the difference between a plume model and measurements as is often done, PSO is run such that each particle is modeled by an unmanned aerial vehicle (UAV) that measures and directly finds the global maximum of the concentration field. Several modifications were made to PSO to allow it to perform successfully in this application. The synthetic data used to test PSO were produced using the 3D building resolving Quick Urban & Industrial Complex Dispersion Modeling System (QUIC), and PSO was implemented in Python. Three different domains were tested: (1) a case with no obstacles, (2) a case with four large obstacles, and (3) a real-world case modeled after the Joint Urban 2003 experiment in Oklahoma City. We found that PSO works well in idealized and real cases. In the Oklahoma City simulation, approximately 90% of the PSO runs with 10 particles make it to within 1% of the maximum domain distance to the source, and approximately 98% of the PSO runs with 50 particles make it to within 1% of the maximum domain distance to the source. However, PSO is not completely immune to local maxima, and there is the possibility of convergence to the wrong point in the domain. The insight from this study can be used to inform first responders or create a tool that can be implemented on UAVs to locate a contaminant source. © 2021 Elsevier Ltd
关键词	Complex urban environment Fast-response urban wind model Lagrangian dispersion model Multi-sensor optimization Particle swarm optimization Source localization
语种	英语
scopus关键词	Antennas; Dispersions; Global optimization; Particle swarm optimization (PSO); Unmanned aerial vehicles (UAV); Complex urban environments; Fast-response urban wind model; Lagrangian dispersion model; Modeling and measurement; Multi-sensor optimization; Oklahoma city; Particle swarm; Plume modeling; Source localization; Swarm optimization; Urban planning; detection method; optimization; pollutant source; simulation; unmanned vehicle; urban area; Oklahoma
来源期刊	ATMOSPHERIC ENVIRONMENT
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/248276
作者单位	Lawrence Livermore National Laboratory, United States; Department of Mechanical Engineering, University of Utah, United States
推荐引用方式 GB/T 7714	Gunawardena N.,Leang K.K.,Pardyjak E.. Particle swarm optimization for source localization in realistic complex urban environments[J],2021,262.
APA	Gunawardena N.,Leang K.K.,&Pardyjak E..(2021).Particle swarm optimization for source localization in realistic complex urban environments.ATMOSPHERIC ENVIRONMENT,262.
MLA	Gunawardena N.,et al."Particle swarm optimization for source localization in realistic complex urban environments".ATMOSPHERIC ENVIRONMENT 262(2021).