DOI: 10.1016/j.atmosenv.2018.01.056
Scopus记录号: 2-s2.0-85041462328
论文题名: Atmospheric dispersion prediction and source estimation of hazardous gas using artificial neural network, particle swarm optimization and expectation maximization
作者: Qiu S ; , Chen B ; , Wang R ; , Zhu Z ; , Wang Y ; , Qiu X
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2018
卷: 178 起始页码: 158
结束页码: 163
语种: 英语
英文关键词: Atmospheric dispersion
; Expectation maximization (EM)
; Neural network
; Particle swarm optimization (PSO)
; Source estimation
Scopus关键词: Atmospheric movements
; Civil defense
; Disasters
; Estimation
; Forecasting
; Hazards
; Maximum principle
; Neural networks
; Risk management
; Atmospheric dispersion
; Concentration distributions
; Current dispersions
; Emergency management
; Expectation - maximizations
; Expectation Maximization
; Source estimation
; Source parameters
; Particle swarm optimization (PSO)
; Article
; artificial neural network
; atmospheric dispersion
; cloud
; gas
; kernel method
; meteorology
; priority journal
; temperature
; velocity
; wind
Scopus学科分类: Environmental Science: Water Science and Technology
; Earth and Planetary Sciences: Earth-Surface Processes
; Environmental Science: Environmental Chemistry
英文摘要: Hazardous gas leak accident has posed a potential threat to human beings. Predicting atmospheric dispersion and estimating its source become increasingly important in emergency management. Current dispersion prediction and source estimation models cannot satisfy the requirement of emergency management because they are not equipped with high efficiency and accuracy at the same time. In this paper, we develop a fast and accurate dispersion prediction and source estimation method based on artificial neural network (ANN), particle swarm optimization (PSO) and expectation maximization (EM). The novel method uses a large amount of pre-determined scenarios to train the ANN for dispersion prediction, so that the ANN can predict concentration distribution accurately and efficiently. PSO and EM are applied for estimating the source parameters, which can effectively accelerate the process of convergence. The method is verified by the Indianapolis field study with a SF6 release source. The results demonstrate the effectiveness of the method. © 2018 Elsevier Ltd Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/82985
Appears in Collections: 气候变化事实与影响
There are no files associated with this item.
作者单位: College of System Engineering, National University of Defense Technology, Changsha, China; Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, XE, Netherlands; College of Territorial Resources and Tourism, Anhui Normal University, Wuhu, China
Recommended Citation:
Qiu S,, Chen B,, Wang R,et al. Atmospheric dispersion prediction and source estimation of hazardous gas using artificial neural network, particle swarm optimization and expectation maximization[J]. Atmospheric Environment,2018-01-01,178